From 67061cf373d84fd21b1acd734345c2145cb0261c Mon Sep 17 00:00:00 2001 From: Jakub Date: Sun, 25 Oct 2026 00:33:24 +0200 Subject: [PATCH] Add Aya 300k learning-rate study and benchmark results Document six completed LR runs as preparation for full Aya training on Perun. Record selection of LR 0.00010 by lowest final validation loss, selected-model lm-eval and partial SK-bench results, reproducible provenance, and dataset-access/context-truncation limitations. Preserve existing 30k and Titan material. --- README.md | 126 + aya_300k_lr_experiments/.gitattributes | 1 + aya_300k_lr_experiments/.gitignore | 9 + .../benchmarks/export/chat_template.jinja | 9 + .../benchmarks/export/config.json | 30 + .../benchmarks/export/export_lr_0p00010.yaml | 12 + .../benchmarks/export/generation_config.json | 6 + .../benchmarks/export/tokenizer_config.json | 19 + .../lmeval/comparison_with_100k.csv | 12 + .../benchmarks/lmeval/full_metrics.csv | 35 + .../logs/lr_0p00010_20261023_224332.log | 58 + .../lmeval/provenance/SHA256SUMS.txt | 18 + .../provenance/comparability_audit.json | 379 + .../provenance/custom_tasks/arc_sk.yaml | 42 + .../provenance/custom_tasks/hellaswag_sk.yaml | 35 + .../provenance/custom_tasks/m_mmlu_sk.yaml | 43 + .../provenance/custom_tasks/sklegal.yaml | 23 + .../provenance/custom_tasks/skquad.yaml | 2 + .../lmeval/provenance/custom_tasks/task.py | 245 + .../custom_tasks/task_configs_from_wandb.json | 232 + .../custom_tasks/truthfulqa_sk_mc1.yaml | 34 + .../custom_tasks/truthfulqa_sk_mc2.yaml | 40 + .../lmeval/provenance/lm_eval_commit.txt | 1 + .../lmeval/provenance/package_versions.json | 8 + .../lmeval/provenance/run_settings.txt | 8 + .../benchmarks/lmeval/provenance/tasks.txt | 1 + .../results_2026-10-24T02-28-32.987157.json | 431 + .../benchmarks/skbench/audit.md | 82 + .../configs/dataset_access_report.json | 622 + .../skbench/configs/export_lr_0p00010.yaml | 12 + .../skbench/configs/model_config.json | 59 + .../skbench/configs/omitted_tasks.txt | 190 + .../skbench/configs/task_configs.json | 13475 +++++++ .../benchmarks/skbench/configs/tasks.txt | 222 + .../skbench/configs/tasks_sk_zero.txt | 412 + .../configs/tasks_sk_zero_available.txt | 225 + .../tasks_sk_zero_available_without_nli.txt | 222 + .../configs/tasks_sk_zero_unavailable.txt | 187 + ...without_nli_lr_0p00010_20261022_185255.log | 31318 ++++++++++++++++ .../skbench/provenance/archive_summary.json | 87 + .../skbench/provenance/audit_skbench.py | 234 + .../provenance/copied_files_sha256.csv | 14 + .../provenance/input_length_diagnostics.py | 62 + .../skbench/provenance/requirements-audit.txt | 1 + .../skbench/provenance/skbench_commit.txt | 1 + .../benchmarks/skbench/provenance/uv.lock | 6274 ++++ .../skbench/results/audit_summary.json | 137 + .../skbench/results/family_prompt_summary.csv | 121 + .../skbench/results/family_summary.csv | 41 + .../input_length_budget_boundary_rows.csv | 70 + .../input_length_diagnostics_per_task.csv | 223 + .../input_length_diagnostics_summary.json | 14 + .../results/log_truncation_evidence.json | 7 + .../skbench/results/per_task_metrics.csv | 223 + .../results_2026-10-22T21-55-04.268099.json | 18023 +++++++++ .../skbench/results/three_prompt_summary.csv | 221 + .../results/truncation_audit_per_task.csv | 223 + .../configs/lr_0p00008.yaml | 50 + .../configs/lr_0p00010.yaml | 50 + .../configs/lr_0p00015.yaml | 50 + .../configs/lr_0p00018.yaml | 50 + 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príkladoch + +Po skúšobnom hľadaní LR pomocou Optuny na 30k príkladoch nasledovalo **overenie šiestich ručne zvolených hodnôt LR na väčšej, fixnej 300k vzorke**. Cieľom bolo nájsť vhodné východiskové nastavenie pre plánovaný tréning na celej slovenskej Aya množine na **Perune**. Tento 300k experiment ešte nebol tréningom na celej množine. + +Podklady k tréningu a následným benchmarkom vybraného modelu sú v priečinku [aya_300k_lr_experiments/](aya_300k_lr_experiments/). Postup opakovania a technické podmienky sú v [reproduction.md](aya_300k_lr_experiments/reproduction.md). + +## Dáta a spoločná konfigurácia + +Zo slovenského `train` splitu `CohereLabs/aya_collection_language_split` sa vyfiltrovali prázdne alebo neplatné `inputs`/`targets`, údaje sa premiešali so seed 42 a vybralo sa 300 000 záznamov. Polia sa previedli na Alpaca formát: `inputs` na `instruction`, prázdny `input` a `targets` na `output`. + +| Parameter | Hodnota | +|---|---| +| Základný model | `slovak-nlp/mistral-sk-7b` | +| Použitý modelový snapshot | `089497ae0d72018e9591895c54f774bf0b4a83a4` | +| Dataset | fixný `aya_slovak_300k_fixed`, bez streamingu | +| Rozdelenie | 299 000 tréningových + 1 000 validačných príkladov | +| Hardvér | Hruška, 1× RTX A4500, GPU 0 | +| Framework a presnosť | LLaMA-Factory + Unsloth, `bf16` | +| Fine-tuning | LoRA, rank 16, alpha 32, dropout 0.0 | +| LoRA moduly | `q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj` | +| Template / cutoff | `alpaca` / 1024 tokenov | +| Packing / train on prompt | `false` / `false` | +| Batch / gradient accumulation | 1 / 16; effective batch 16 | +| Tréning | 1 epocha, 18 688 optimizer krokov | +| Optimizer / scheduler | `adamw_torch` / cosine | +| Warmup / max grad norm | 1500 krokov / 0.5 | +| Evaluácia / logovanie | každých 1000 / 10 krokov + finálna evaluácia | +| Seed | 42 | + +Pôvodné [konfigurácie](aya_300k_lr_experiments/configs/) a [tréningové logy](aya_300k_lr_experiments/logs/) sú zachované. Konfigurácie sa líšia hodnotou LR, výstupnou cestou a prevádzkovým príznakom `overwrite_output_dir`. Archív neobsahuje checksum vstupného datasetu ani identifikátory validačných vzoriek; úplnú identitu dát preto nemožno nezávisle overiť iba z uložených konfigurácií. + +## Výsledky a výber LR pre ďalší tréning + +Prvá séria obsahovala LR `0.00018`, `0.00020` a `0.00023`. Po jej vyhodnotení sa rozsah rozšíril smerom nadol o `0.00015`, `0.00010` a `0.00008`. Všetkých šesť behov dokončilo jednu epochu. + +| Learning rate | Finálny eval loss | Minimálny eval loss | Krok minima | +|---:|---:|---:|---:| +| 0.00008 | 0.875217 | 0.875217 | 18 688 | +| **0.00010** | **0.872620** | **0.872620** | **18 688** | +| 0.00015 | 0.884270 | 0.884270 | 18 688 | +| 0.00018 | 0.891460 | 0.891388 | 18 000 | +| 0.00020 | 0.897669 | 0.897507 | 18 000 | +| 0.00023 | 0.903236 | 0.903193 | 18 000 | + +**LR `0.00010` bol vybraný preto, že dosiahol najnižší finálny validačný loss zo všetkých šiestich 300k behov: `0.87261962890625`.** Rozdiel oproti LR `0.00008` je približne 0.002597. Ide o najlepší bodový výsledok v tejto sérii; neprebehli opakovania s viacerými seedmi ani overenie optimálneho LR na celej Aya množine. Pre Perun slúži ako východisko. + +Na 30k skúške bol najlepší skúšaný LR `0.0002`. Výsledky 300k série preto ukazujú význam overenia LR na väčšej vzorke. Absolútne hodnoty eval loss medzi 30k, 100k a 300k sériami nie sú priamym porovnaním kvality modelov, keďže nemožno predpokladať zhodné validačné vzorky. + +Presné údaje sú v [pôvodnom LR súhrne](aya_300k_lr_experiments/results/aya_300k_lr_summary.csv), [rozšírenom súhrne behov](aya_300k_lr_experiments/results/training_run_summary.csv) a [histórii validačného loss](aya_300k_lr_experiments/results/eval_loss_history.csv). + +

+ Finálny eval loss podľa learning rate na 300k Aya príkladoch +

+ +

+ Priebeh validačného loss pre šesť learning rate +

+ +

+ Priebeh zaznamenaného gradient norm pre šesť learning rate +

+ +## Export vybraného modelu a benchmarky + +Na následné benchmarky sa vybral model s LR `0.00010`. Jeho LoRA adaptér sa zlúčil s pôvodným základným modelom do samostatného modelu v `bfloat16`. Export nepredstavoval ďalší tréning. [Exportná konfigurácia](aya_300k_lr_experiments/benchmarks/export/export_lr_0p00010.yaml) používa rovnaký snapshot základného modelu a template `alpaca`. + +Benchmarky skúmajú schopnosti tohto vybraného modelu. **Ostatných päť LR zatiaľ týmito benchmarkmi nebolo porovnaných**, takže z nich nemožno vyhlásiť LR `0.00010` za najlepší LR podľa downstream metrík. + +### lm-evaluation-harness + +Použilo sa pôvodné prostredie `lm_eval_hruska`, HF backend, `bf16`, batch size 1 a zero-shot nastavenie bez aplikovania chat template. Zlúčený model sa načítal samostatne; tokenizer pochádzal z pôvodného modelového snapshotu. Tento postup obchádza nekompatibilitu exportovaného označenia `TokenizersBackend` so starším Transformers 4.48.2. + +Vyhodnotenie dokončilo všetkých sedem pôvodných úloh a skončilo s návratovým kódom 0. Zachované sú [výsledky](aya_300k_lr_experiments/benchmarks/lmeval/results/), [log](aya_300k_lr_experiments/benchmarks/lmeval/logs/), custom úlohy a metadáta prostredia. V nasledujúcej tabuľke sú accuracy metriky prevedené na percentá; SKQuad F1 je už v zdroji v škále 0–100. + +| Model | ARC acc_norm | HellaSwag acc_norm | M-MMLU acc | TQA MC1 | TQA MC2 | SKLegal acc_norm | SKQuad F1 | +|---|---:|---:|---:|---:|---:|---:|---:| +| 100k – experiment 01, baseline | 34.05 | 52.09 | 35.50 | 17.99 | 34.39 | 46.85 | 36.68 | +| 100k – experiment 08, attention-only | 35.24 | 52.18 | 34.48 | 18.77 | 36.74 | 47.68 | 43.38 | +| 100k – experiment 10, r16/a32/LR 0.0001 | 32.93 | 51.57 | 34.48 | 20.57 | 34.80 | 46.18 | 37.76 | +| **300k – vybraný LR 0.00010** | 34.73 | 51.64 | 31.03 | 19.67 | 35.07 | 46.10 | 38.73 | + +Oproti 100k experimentu 10 dosiahol vybraný 300k model bodovo vyššie ARC acc_norm (+1.80 percentuálneho bodu) a SKQuad F1 (+0.97 bodu), ale nižšie M-MMLU acc (−3.45 percentuálneho bodu). Ostatné zmeny sú menšie. V žiadnej zo siedmich hlavných metrík neprekonal maximum z celej desiatky 100k behov; tieto maximá navyše pochádzajú z rôznych konfigurácií. Výber najnižšieho validačného loss teda neznamená automaticky najlepší výsledok v každej downstream úlohe. Nejde o vyhodnotenie všetkých šiestich 300k LR ani o test štatistickej významnosti rozdielov. + +Úplné porovnanie so všetkými desiatimi staršími Hruška experimentmi je v [comparison_with_100k.csv](aya_300k_lr_experiments/benchmarks/lmeval/comparison_with_100k.csv). Vo všetkých porovnávaných Hruška behoch sa zhodujú konfigurácie a verzie úloh, počty vzoriek, seeds, evaluator commit `6d2abda4`, Transformers 4.48.2 a nastavenie bez chat template. Pole `task_hashes` je však v zdrojových JSON súboroch prázdne, takže nezávislé overenie obsahu benchmarkových dát pomocou hashov nie je možné. Staršie výsledky používali samostatný PEFT adaptér, nový výsledok zlúčený model v BF16; spôsob načítania a zaokrúhlenie pri zlúčení preto nie sú úplne identické. Podrobnosti sú v [audite porovnateľnosti](aya_300k_lr_experiments/benchmarks/lmeval/provenance/comparability_audit.json). + +### SK-bench + +Dokončil sa **čiastkový slovenský zero-shot beh s 222 položkami**. Položka predstavuje kombináciu úlohy, podúlohy a variantu promptu, nie jeden hodnotený príklad. Zahrnuté boli tri slovenské formulácie promptov dostupných úloh. Použila sa Alpaca šablóna zlúčeného modelu, greedy dekódovanie, seed 1234, `bf16`, jedna GPU a maximálny kontext 32 768 tokenov. + +Z pôvodných 412 slovenských položiek sa vynechalo 187 položiek pre 12 nedostupných repozitárov: Chiby, Autoškola, e-testy, IFEval-SK, OneRuler-SK, Scio, Slohry, Slovak school exams, Slovak Trivia, SlovakSum, SME-Sum a slovné analógie. Ich kontrola vracala `RepositoryNotFoundError`; tento stav sám osebe nerozlišuje neexistujúci repozitár od súkromného repozitára bez oprávnenia. Ďalšie tri položky NLI-SK-annotated sa vynechali, pretože prístup vyžadoval schválenie autorkou a pri spúšťaní behu ešte nebol udelený. Ostatné NLI úlohy zo skLEP v behu zostali. + +Uložené sú [vykonané úlohy](aya_300k_lr_experiments/benchmarks/skbench/configs/tasks.txt), [vynechané úlohy](aya_300k_lr_experiments/benchmarks/skbench/configs/omitted_tasks.txt), [report dostupnosti](aya_300k_lr_experiments/benchmarks/skbench/configs/dataset_access_report.json), úplný výsledný JSON a úspešný log. Audit 222 detailných parquet výstupov overil **159 426 hodnotení** (53 142 pre každý z troch promptov). **Log zároveň potvrdzuje orezanie príliš dlhého vstupu.** Všetky uložené `model_response.truncated_tokens_count` sú síce nulové, ale tieto počítadlá nezachytili orezanie hlásené enginom. Pri MultiWikiQA má 69 vstupov dĺžku presne na hranici použitého rozpočtu (23 pri každom prompte); ide o kandidátov konzistentných s orezaním, nie o spoľahlivo dokázaný počet orezaných príkladov. Výsledky dlhých QA vstupov preto treba interpretovať s týmto obmedzením. Podrobnosti sú v [audite](aya_300k_lr_experiments/benchmarks/skbench/results/audit_summary.json) a [počítadlách po úlohách](aya_300k_lr_experiments/benchmarks/skbench/results/truncation_audit_per_task.csv). + +Výber opisných výsledkov z dostupných úloh. Uvedený priemer je vážený počtom hodnotených príkladov v príslušných variantoch; všetky tri prompty majú v danej úlohe rovnaký počet príkladov. Pri MMLU zahŕňa všetkých 57 predmetov a tri prompty. Toto nie je oficiálna agregácia úplného SK-bench. + +| Úloha | Metrika | Počet variantov | Priemer (%) | +|---|---|---:|---:| +| ARC | `em_relaxed` | 3 | 19.08 | +| Belebele | `em_relaxed` | 3 | 21.33 | +| Financial Exams | `em_relaxed` | 3 | 22.94 | +| Global-MMLU-Lite | `em_relaxed` | 3 | 19.83 | +| MMLU (57 predmetov) | `em_relaxed` | 171 | 15.72 | +| SlovakCOPA | `em_relaxed` | 3 | 40.47 | +| SKJ1 | `em_relaxed` | 3 | 4.33 | +| TruthfulQA MC1 | `em_relaxed` | 3 | 1.20 | +| MovieReview | `em_norm_relaxed` | 3 | 40.90 | +| ToxicSK | `em_norm_relaxed` | 3 | 47.60 | +| MultiWikiQA | `f1_slk` | 3 | 50.99 | +| skLEP-QA | `f1_slk` | 3 | 37.50 | + +Úplné metriky sú v [per_task_metrics.csv](aya_300k_lr_experiments/benchmarks/skbench/results/per_task_metrics.csv); rodinné a promptové súhrny vrátane nevážených priemerov sú v [family_summary.csv](aya_300k_lr_experiments/benchmarks/skbench/results/family_summary.csv) a [family_prompt_summary.csv](aya_300k_lr_experiments/benchmarks/skbench/results/family_prompt_summary.csv). + +Výsledky treba čítať spolu s definíciou metriky a promptu. Napríklad pri ARC je priemer presnej metriky `em` približne 2.37 %, zatiaľ čo `em_relaxed` dosahuje 19.08 %. Rozdiel ukazuje citlivosť generatívneho hodnotenia na spôsob zápisu odpovede. Úplný surový JSON zachováva aj agregát `all`, ktorý sa tu neinterpretuje ako celkové skóre: mieša rôzne metriky a pokrýva neúplnú množinu úloh. Samotné MMLU tvorí 171 z 222 položiek, preto jednoduché spriemerovanie všetkých riadkov nie je vyváženým súhrnom schopností modelu. + +**Tento výsledok nie je oficiálne celkové skóre úplného SK-bench.** Pri porovnávaní ďalších modelov treba použiť rovnaký zmrazený zoznam úloh a rovnaký protokol. SK-bench používa generovanie a následné porovnávanie textovej odpovede, zatiaľ čo väčšina uvedených lm-eval úloh vyhodnocuje pravdepodobnosti možností. Odlišné sú aj konkrétne datasety, prompty a metriky; hodnoty medzi týmito dvoma frameworkmi preto nie sú priamo zameniteľné. + +Pri príprave sa sampler FlashInfer pokúšal kompilovať cez starší systémový CUDA toolkit. Úspešný beh používal `VLLM_USE_FLASHINFER_SAMPLER=0` a `--enforce-eager`. Podrobné verzie a parametre sú súčasťou benchmarkových metadát. + +## Uložené podklady + +Priečinok [aya_300k_lr_experiments/](aya_300k_lr_experiments/) obsahuje pôvodné konfigurácie, tréningové a benchmarkové logy, výsledné JSON/CSV súbory, grafy, vybrané metadáta a skripty. Veľké váhy, checkpointy, `trainer_state.json`, kompletné per-sample parquet súbory a duplicitné inference cache zostávajú v pôvodnom archíve. Grafy a odvodené tabuľky tréningu možno obnoviť z malých uložených histórií bez prístupu k váham modelov. + +Zdrojový archív a jeho SHA-256 sú zaznamenané v [source_archive.json](aya_300k_lr_experiments/provenance/source_archive.json). diff --git a/aya_300k_lr_experiments/.gitattributes b/aya_300k_lr_experiments/.gitattributes new file mode 100644 index 0000000..dfdb8b7 --- /dev/null +++ b/aya_300k_lr_experiments/.gitattributes @@ -0,0 +1 @@ +*.sh text eol=lf diff --git a/aya_300k_lr_experiments/.gitignore b/aya_300k_lr_experiments/.gitignore new file mode 100644 index 0000000..10b8036 --- /dev/null +++ b/aya_300k_lr_experiments/.gitignore @@ -0,0 +1,9 @@ +__pycache__/ +*.pyc +*.safetensors +*.bin +*.pt +*.pth +*.parquet +checkpoint-*/ +trainer_state.json diff --git a/aya_300k_lr_experiments/benchmarks/export/chat_template.jinja b/aya_300k_lr_experiments/benchmarks/export/chat_template.jinja new file mode 100644 index 0000000..8d7a085 --- /dev/null +++ b/aya_300k_lr_experiments/benchmarks/export/chat_template.jinja @@ -0,0 +1,9 @@ +{% set system_message = 'Below is an instruction that describes a task. Write a response that appropriately completes the request. + +' %}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% endif %}{% if system_message is defined %}{{ system_message }}{% endif %}{% for message in loop_messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '### Instruction: +' + content + ' + +### Response: +' }}{% elif message['role'] == 'assistant' %}{{ content + '' + ' + +' }}{% endif %}{% endfor %} \ No newline at end of file diff --git a/aya_300k_lr_experiments/benchmarks/export/config.json b/aya_300k_lr_experiments/benchmarks/export/config.json new file mode 100644 index 0000000..13eab00 --- /dev/null +++ b/aya_300k_lr_experiments/benchmarks/export/config.json @@ -0,0 +1,30 @@ +{ + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} diff --git a/aya_300k_lr_experiments/benchmarks/export/export_lr_0p00010.yaml b/aya_300k_lr_experiments/benchmarks/export/export_lr_0p00010.yaml new file mode 100644 index 0000000..fcbcfa7 --- /dev/null +++ b/aya_300k_lr_experiments/benchmarks/export/export_lr_0p00010.yaml @@ -0,0 +1,12 @@ +model_name_or_path: slovak-nlp/mistral-sk-7b +model_revision: 089497ae0d72018e9591895c54f774bf0b4a83a4 +adapter_name_or_path: /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00010 + +template: alpaca +finetuning_type: lora +infer_dtype: bfloat16 + +export_dir: /home/schwarc/diplomovka/skbench_evaluation/models/aya_300k_lr_0p00010 +export_size: 5 +export_device: cpu +export_legacy_format: false diff --git a/aya_300k_lr_experiments/benchmarks/export/generation_config.json b/aya_300k_lr_experiments/benchmarks/export/generation_config.json new file mode 100644 index 0000000..b7715d4 --- /dev/null +++ b/aya_300k_lr_experiments/benchmarks/export/generation_config.json @@ -0,0 +1,6 @@ +{ + "_from_model_config": true, + "bos_token_id": 1, + "eos_token_id": 2, + "transformers_version": "5.5.0" +} diff --git a/aya_300k_lr_experiments/benchmarks/export/tokenizer_config.json b/aya_300k_lr_experiments/benchmarks/export/tokenizer_config.json new file mode 100644 index 0000000..6a1eb78 --- /dev/null +++ b/aya_300k_lr_experiments/benchmarks/export/tokenizer_config.json @@ -0,0 +1,19 @@ +{ + "add_prefix_space": true, + 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a/aya_300k_lr_experiments/benchmarks/lmeval/full_metrics.csv b/aya_300k_lr_experiments/benchmarks/lmeval/full_metrics.csv new file mode 100644 index 0000000..e76867e --- /dev/null +++ b/aya_300k_lr_experiments/benchmarks/lmeval/full_metrics.csv @@ -0,0 +1,35 @@ +model_id,training_samples,task,metric,value,unit,status +aya_300k_lr_0p00010,300000,arc_sk,"acc,none",0.30025662959794697,0..1,reported +aya_300k_lr_0p00010,300000,arc_sk,"acc_stderr,none",0.013412022629366669,0..1,reported +aya_300k_lr_0p00010,300000,arc_sk,"acc_norm,none",0.3473053892215569,0..1,reported +aya_300k_lr_0p00010,300000,arc_sk,"acc_norm_stderr,none",0.013931226499492353,0..1,reported +aya_300k_lr_0p00010,300000,hellaswag_sk,"acc,none",0.4083289404322615,0..1,reported +aya_300k_lr_0p00010,300000,hellaswag_sk,"acc_stderr,none",0.005047187883274763,0..1,reported +aya_300k_lr_0p00010,300000,hellaswag_sk,"acc_norm,none",0.5163943068002108,0..1,reported 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+aya_300k_lr_0p00010,300000,skquad,"HasAns_exact,none",18.540838569047313,0..100,reported +aya_300k_lr_0p00010,300000,skquad,"HasAns_exact_stderr,none",N/A,0..100,unavailable +aya_300k_lr_0p00010,300000,skquad,"HasAns_f1,none",47.549942420326644,0..100,reported +aya_300k_lr_0p00010,300000,skquad,"HasAns_f1_stderr,none",N/A,0..100,unavailable +aya_300k_lr_0p00010,300000,skquad,"NoAns_exact,none",0.1681614349775785,0..100,reported +aya_300k_lr_0p00010,300000,skquad,"NoAns_exact_stderr,none",N/A,0..100,unavailable +aya_300k_lr_0p00010,300000,skquad,"NoAns_f1,none",0.1681614349775785,0..100,reported +aya_300k_lr_0p00010,300000,skquad,"NoAns_f1_stderr,none",N/A,0..100,unavailable +aya_300k_lr_0p00010,300000,skquad,"best_exact,none",18.75195658979443,0..100,reported +aya_300k_lr_0p00010,300000,skquad,"best_exact_stderr,none",N/A,0..100,unavailable +aya_300k_lr_0p00010,300000,skquad,"best_f1,none",38.7292080701372,0..100,reported +aya_300k_lr_0p00010,300000,skquad,"best_f1_stderr,none",N/A,0..100,unavailable +aya_300k_lr_0p00010,300000,truthfulqa_sk_mc1,"acc,none",0.19665809768637532,0..1,reported +aya_300k_lr_0p00010,300000,truthfulqa_sk_mc1,"acc_stderr,none",0.014259215011433178,0..1,reported +aya_300k_lr_0p00010,300000,truthfulqa_sk_mc2,"acc,none",0.3506542164316134,0..1,reported +aya_300k_lr_0p00010,300000,truthfulqa_sk_mc2,"acc_stderr,none",0.014662940445997986,0..1,reported diff --git a/aya_300k_lr_experiments/benchmarks/lmeval/logs/lr_0p00010_20261023_224332.log b/aya_300k_lr_experiments/benchmarks/lmeval/logs/lr_0p00010_20261023_224332.log new file mode 100644 index 0000000..b7fde91 --- /dev/null +++ b/aya_300k_lr_experiments/benchmarks/lmeval/logs/lr_0p00010_20261023_224332.log @@ -0,0 +1,58 @@ +2026-10-23:22:43:38,177 INFO [lm_eval.__main__:307] Including path: /home/schwarc/diplomovka/lmeval_slovak_tasks +2026-10-23:22:43:47,185 INFO [lm_eval.__main__:379] Selected Tasks: ['arc_sk', 'hellaswag_sk', 'm_mmlu_sk', 'sklegal', 'skquad', 'truthfulqa_sk_mc1', 'truthfulqa_sk_mc2'] +2026-10-23:22:43:47,186 INFO [lm_eval.evaluator:169] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234 | Setting fewshot manual seed to 1234 +2026-10-23:22:43:47,186 INFO [lm_eval.evaluator:206] Initializing hf model, with arguments: {'pretrained': '/home/schwarc/diplomovka/skbench_evaluation/models/aya_300k_lr_0p00010', 'tokenizer': '/home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4', 'dtype': 'bfloat16'} +2026-10-23:22:43:47,315 INFO [lm_eval.models.huggingface:136] Using device 'cuda:0' +2026-10-23:22:43:47,566 INFO [lm_eval.models.huggingface:376] Model parallel was set to False, max memory was not set, and device map was set to {'': 'cuda:0'} + Loading checkpoint shards: 0%| | 0/3 [00:00 datasets.Dataset:\n\ + \ def _process_doc(doc):\n # breakpoint()\n out_doc = {\n \ + \ \"id\": doc[\"id\"],\n \"query\": \"Question: \" + preprocess(doc[\"\ + instruction\"]) + \"\\nAnswer:\",\n \"choices\": [\n preprocess(option)\n\ + \ for option in [\n doc[\"option_a\"],\n \ + \ doc[\"option_b\"],\n doc[\"option_c\"],\n \ + \ doc[\"option_d\"],\n doc[\"option_e\"],\n \ + \ ]\n if option\n ],\n \"gold\"\ + : [\"A\", \"B\", \"C\", \"D\", \"E\"].index(doc[\"answer\"]),\n }\n \ + \ return out_doc\n\n return dataset.map(_process_doc)\n" +doc_to_choice: choices +doc_to_target: gold +training_split: train +target_delimiter: ' ' +validation_split: validation +fewshot_delimiter: ' + + + ' +should_decontaminate: true +doc_to_decontamination_query: query diff --git a/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/hellaswag_sk.yaml b/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/hellaswag_sk.yaml new file mode 100644 index 0000000..2c96e72 --- /dev/null +++ b/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/hellaswag_sk.yaml @@ -0,0 +1,35 @@ +tag: +- hellaswag_multilingual +task: hellaswag_sk +repeats: 1 +metadata: + version: 1 +description: '' +doc_to_text: query +metric_list: +- metric: acc + aggregation: mean + higher_is_better: true +- metric: acc_norm + aggregation: mean + higher_is_better: true +num_fewshot: 0 +output_type: multiple_choice +unsafe_code: false +dataset_name: sk +dataset_path: alexandrainst/m_hellaswag +process_docs: "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n\ + \ def _process_doc(doc):\n ctx = doc[\"ctx_a\"] + \" \" + doc[\"ctx_b\"\ + ].capitalize()\n out_doc = {\n \"query\": preprocess(doc[\"activity_label\"\ + ] + \": \" + ctx),\n \"choices\": [preprocess(ending) for ending in doc[\"\ + endings\"]],\n \"gold\": int(doc[\"label\"]),\n }\n return\ + \ out_doc\n\n return dataset.map(_process_doc)\n" +doc_to_choice: choices +doc_to_target: '{{label.lstrip()}}' +target_delimiter: ' ' +validation_split: val +fewshot_delimiter: ' + + + ' +should_decontaminate: false diff --git a/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/m_mmlu_sk.yaml b/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/m_mmlu_sk.yaml new file mode 100644 index 0000000..501237a --- /dev/null +++ b/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/m_mmlu_sk.yaml @@ -0,0 +1,43 @@ +tag: +- m_mmlu +task: m_mmlu_sk +repeats: 1 +metadata: + version: 0 +test_split: test +description: '' +doc_to_text: '{{instruction.strip()}} + + A. {{option_a}} + + B. {{option_b}} + + C. {{option_c}} + + D. {{option_d}} + + Answer:' +metric_list: +- metric: acc + aggregation: mean + higher_is_better: true +num_fewshot: 0 +output_type: multiple_choice +unsafe_code: false +dataset_name: sk +dataset_path: alexandrainst/m_mmlu +doc_to_choice: +- A +- B +- C +- D +doc_to_target: answer +fewshot_split: train +fewshot_config: + sampler: first_n +target_delimiter: ' ' +fewshot_delimiter: ' + + + ' +should_decontaminate: false diff --git a/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/sklegal.yaml b/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/sklegal.yaml new file mode 100644 index 0000000..814685f --- /dev/null +++ b/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/sklegal.yaml @@ -0,0 +1,23 @@ +task: sklegal +repeats: 1 +metadata: + version: 1 +test_split: test +description: '' +doc_to_text: '{{prompt}} ' +metric_list: + - metric: acc + aggregation: mean + higher_is_better: true + - metric: acc_norm + aggregation: mean + higher_is_better: true +num_fewshot: 0 +output_type: multiple_choice +unsafe_code: false +dataset_path: TUKE-KEMT/slovak-legal-exam +doc_to_choice: '{{answers}}' +doc_to_target: label +target_delimiter: ' ' +fewshot_delimiter: "\n\n" +should_decontaminate: false diff --git a/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/skquad.yaml b/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/skquad.yaml new file mode 100644 index 0000000..2546a99 --- /dev/null +++ b/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/skquad.yaml @@ -0,0 +1,2 @@ +task: skquad +class: !function task.SQuAD2 diff --git a/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/task.py b/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/task.py new file mode 100644 index 0000000..c9e55f7 --- /dev/null +++ b/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/task.py @@ -0,0 +1,245 @@ +""" +Know What You Don’t Know: Unanswerable Questions for SQuAD +https://arxiv.org/pdf/1806.03822.pdf + +Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, +consisting of questions posed by crowdworkers on a set of Wikipedia articles, +where the answer to every question is a segment of text, or span, from the +corresponding reading passage, or the question might be unanswerable. +SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable +questions written adversarially by crowdworkers to look similar to answerable ones. +To do well on SQuAD2.0, systems must not only answer questions when possible, but +also determine when no answer is supported by the paragraph and abstain from answering. + +Homepage: https://rajpurkar.github.io/SQuAD-explorer/ +""" + +from functools import partial +from math import exp + +import datasets +from packaging import version + +from lm_eval.api.instance import Instance +from lm_eval.api.task import ConfigurableTask + + +_CITATION = """ +@misc{rajpurkar2018know, + title={Know What You Don't Know: Unanswerable Questions for SQuAD}, + author={Pranav Rajpurkar and Robin Jia and Percy Liang}, + year={2018}, + eprint={1806.03822}, + archivePrefix={arXiv}, + primaryClass={cs.CL} +} +""" + + +def _squad_metric(predictions, references): + import evaluate + + squad_metric = evaluate.load("squad_v2") + return squad_metric.compute(predictions=predictions, references=references) + + +def _squad_agg(key, items): + predictions, references = zip(*items) + + return _squad_metric(predictions=predictions, references=references).get(key, 0) + + +class SQuAD2(ConfigurableTask): + VERSION = 3 + DATASET_PATH = "TUKE-DeutscheTelekom/skquad" + DATASET_NAME = None + + def __init__(self, config=None): + super().__init__(config={"metadata": {"version": self.VERSION}}) + + # HF changed squad on us so we have to make sure we aren't running the old one + assert version.parse(datasets.__version__) >= version.parse("1.11.0"), ( + "datasets v1.11.0 or later required for SQuAD" + ) + + def has_training_docs(self): + return True + + def has_validation_docs(self): + return True + + def has_test_docs(self): + return False + + def training_docs(self): + return self.dataset["train"] + + def validation_docs(self): + return self.dataset["validation"] + + def doc_to_text(self, doc): + return ( + "Title: " + + doc["title"] + + "\n\n" + + "Background: " + + doc["context"] + + "\n\n" + + "Question: " + + doc["question"] + + "\n\n" + + "Answer:" + ) + + def should_decontaminate(self): + return True + + def doc_to_decontamination_query(self, doc): + return doc["context"] + + def doc_to_target(self, doc): + answer_list = doc["answers"]["text"] + if len(answer_list) > 0: + answer = answer_list[0] + else: + answer = "unanswerable" + return " " + answer + + def construct_requests( + self, doc, ctx, chat_template=None, apply_chat_template=False, **kwargs + ): + """Uses RequestFactory to construct Requests and returns an iterable of + Requests which will be sent to the LM. + + :param doc: + The document as returned from training_docs, validation_docs, or test_docs. + :param ctx: str + The context string, generated by fewshot_context. This includes the natural + language description, as well as the few shot examples, and the question + part of the document for `doc`. + """ + + return [ + Instance( + request_type="generate_until", + doc=doc, + arguments=(ctx, {"until": ["\n"]}), + idx=0, + **kwargs, + ), + Instance( + request_type="loglikelihood", + doc=doc, + arguments=(ctx, " " + "unanswerable"), + idx=0, + **kwargs, + ), + ] + + def process_results(self, doc, results): + """Take a single document and the LM results and evaluates, returning a + dict where keys are the names of submetrics and values are the values of + the metric for that one document + + :param doc: + The document as returned from training_docs, validation_docs, or test_docs. + :param results: + The results of the requests created in construct_requests. + """ + + continuation, (logprob_unanswerable, _) = results + + no_answer_probability = exp(logprob_unanswerable) + + predictions = { + "id": doc["id"], + "prediction_text": continuation, + "no_answer_probability": no_answer_probability, + } + + references = { + "id": doc["id"], + "answers": doc["answers"], + } + + return { + "exact": ( + predictions, + references, + ), # Exact match (the normalized answer exactly match the gold answer) + "f1": ( + predictions, + references, + ), # The F-score of predicted tokens versus the gold answer + "HasAns_exact": ( + predictions, + references, + ), # Exact match (the normalized answer exactly match the gold answer) + "HasAns_f1": ( + predictions, + references, + ), # The F-score of predicted tokens versus the gold answer + "NoAns_exact": ( + predictions, + references, + ), # Exact match (the normalized answer exactly match the gold answer) + "NoAns_f1": ( + predictions, + references, + ), # The F-score of predicted tokens versus the gold answer + "best_exact": ( + predictions, + references, + ), # Best exact match (with varying threshold) + "best_f1": (predictions, references), # Best F1 (with varying threshold) + } + + def aggregation(self): + """ + :returns: {str: [float] -> float} + A dictionary where keys are the names of submetrics and values are + functions that aggregate a list of metrics + """ + return { + "exact": partial( + _squad_agg, "exact" + ), # Exact match (the normalized answer exactly match the gold answer) + "f1": partial( + _squad_agg, "f1" + ), # The F-score of predicted tokens versus the gold answer + "HasAns_exact": partial( + _squad_agg, "HasAns_exact" + ), # Exact match (the normalized answer exactly match the gold answer) + "HasAns_f1": partial( + _squad_agg, "HasAns_f1" + ), # The F-score of predicted tokens versus the gold answer + "NoAns_exact": partial( + _squad_agg, "NoAns_exact" + ), # Exact match (the normalized answer exactly match the gold answer) + "NoAns_f1": partial( + _squad_agg, "NoAns_f1" + ), # The F-score of predicted tokens versus the gold answer + "best_exact": partial( + _squad_agg, "best_exact" + ), # Best exact match (with varying threshold) + "best_f1": partial( + _squad_agg, "best_f1" + ), # Best F1 (with varying threshold) + } + + def higher_is_better(self): + """ + :returns: {str: bool} + A dictionary where keys are the names of submetrics and values are + whether a higher value of the submetric is better + """ + return { + "exact": True, # Exact match (the normalized answer exactly match the gold answer) + "f1": True, # The F-score of predicted tokens versus the gold answer + "HasAns_exact": True, # Exact match (the normalized answer exactly match the gold answer) + "HasAns_f1": True, # The F-score of predicted tokens versus the gold answer + "NoAns_exact": True, # Exact match (the normalized answer exactly match the gold answer) + "NoAns_f1": True, # The F-score of predicted tokens versus the gold answer + "best_exact": True, # Best exact match (with varying threshold) + "best_f1": True, # Best F1 (with varying threshold) + } diff --git a/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/task_configs_from_wandb.json b/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/task_configs_from_wandb.json new file mode 100644 index 0000000..99a3cb3 --- /dev/null +++ b/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/task_configs_from_wandb.json @@ -0,0 +1,232 @@ +{ + "arc_sk": { + "tag": [ + "arc_multilingual" + ], + "task": "arc_sk", + "repeats": 1, + "metadata": { + "version": 2 + }, + "test_split": "test", + "description": "", + "doc_to_text": "query", + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "acc_norm", + "aggregation": "mean", + "higher_is_better": true + } + ], + "num_fewshot": 0, + "output_type": "multiple_choice", + "unsafe_code": false, + "dataset_name": "sk", + "dataset_path": "alexandrainst/m_arc", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc):\n # breakpoint()\n out_doc = {\n \"id\": doc[\"id\"],\n \"query\": \"Question: \" + preprocess(doc[\"instruction\"]) + \"\\nAnswer:\",\n \"choices\": [\n preprocess(option)\n for option in [\n doc[\"option_a\"],\n doc[\"option_b\"],\n doc[\"option_c\"],\n doc[\"option_d\"],\n doc[\"option_e\"],\n ]\n if option\n ],\n \"gold\": [\"A\", \"B\", \"C\", \"D\", \"E\"].index(doc[\"answer\"]),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", + "doc_to_choice": "choices", + "doc_to_target": "gold", + "training_split": "train", + "target_delimiter": " ", + "validation_split": "validation", + "fewshot_delimiter": "\n\n", + "should_decontaminate": true, + "doc_to_decontamination_query": "query" + }, + "skquad": { + "task": "skquad", + "repeats": 1, + "metadata": { + "version": 3 + }, + "description": "", + "num_fewshot": 0, + "output_type": "generate_until", + "unsafe_code": false, + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "generation_kwargs": { + "until": [ + "\n\n" + ], + "do_sample": false + }, + "should_decontaminate": false + }, + "sklegal": { + "task": "sklegal", + "repeats": 1, + "metadata": { + "version": 1 + }, + "test_split": "test", + "description": "", + "doc_to_text": "{{prompt}} ", + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "acc_norm", + "aggregation": "mean", + "higher_is_better": true + } + ], + "num_fewshot": 0, + "output_type": "multiple_choice", + "unsafe_code": false, + "dataset_path": "TUKE-KEMT/slovak-legal-exam", + "doc_to_choice": "{{answers}}", + "doc_to_target": "label", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "should_decontaminate": false + }, + "m_mmlu_sk": { + "tag": [ + "m_mmlu" + ], + "task": "m_mmlu_sk", + "repeats": 1, + "metadata": { + "version": 0 + }, + "test_split": "test", + "description": "", + "doc_to_text": "{{instruction.strip()}}\nA. {{option_a}}\nB. {{option_b}}\nC. {{option_c}}\nD. {{option_d}}\nAnswer:", + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "num_fewshot": 0, + "output_type": "multiple_choice", + "unsafe_code": false, + "dataset_name": "sk", + "dataset_path": "alexandrainst/m_mmlu", + "doc_to_choice": [ + "A", + "B", + "C", + "D" + ], + "doc_to_target": "answer", + "fewshot_split": "train", + "fewshot_config": { + "sampler": "first_n" + }, + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "should_decontaminate": false + }, + "hellaswag_sk": { + "tag": [ + "hellaswag_multilingual" + ], + "task": "hellaswag_sk", + "repeats": 1, + "metadata": { + "version": 1 + }, + "description": "", + "doc_to_text": "query", + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "acc_norm", + "aggregation": "mean", + "higher_is_better": true + } + ], + "num_fewshot": 0, + "output_type": "multiple_choice", + "unsafe_code": false, + "dataset_name": "sk", + "dataset_path": "alexandrainst/m_hellaswag", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc):\n ctx = doc[\"ctx_a\"] + \" \" + doc[\"ctx_b\"].capitalize()\n out_doc = {\n \"query\": preprocess(doc[\"activity_label\"] + \": \" + ctx),\n \"choices\": [preprocess(ending) for ending in doc[\"endings\"]],\n \"gold\": int(doc[\"label\"]),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", + "doc_to_choice": "choices", + "doc_to_target": "{{label.lstrip()}}", + "target_delimiter": " ", + "validation_split": "val", + "fewshot_delimiter": "\n\n", + "should_decontaminate": false + }, + "truthfulqa_sk_mc1": { + "tag": [ + "truthfulqa_multilingual" + ], + "task": "truthfulqa_sk_mc1", + "repeats": 1, + "metadata": { + "version": 1 + }, + "description": "", + "doc_to_text": "query", + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "num_fewshot": 0, + "output_type": "multiple_choice", + "unsafe_code": false, + "dataset_name": "sk", + "dataset_path": "alexandrainst/m_truthfulqa", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc):\n out_doc = {\n \"question\": preprocess(doc[\"question\"]),\n \"query\": QA_PROMPT + \"\\n\\nQ: \" + preprocess(doc[\"question\"]) + \"\\nA:\",\n \"mc1_choices\": doc[\"mc1_targets_choices\"],\n \"mc2_choices\": doc[\"mc2_targets_choices\"],\n \"mc2_targets\": {\"labels\": doc[\"mc2_targets_labels\"]},\n \"gold\": \" \",\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", + "doc_to_choice": "mc1_choices", + "doc_to_target": 0, + "target_delimiter": " ", + "validation_split": "val", + "fewshot_delimiter": "\n\n", + "should_decontaminate": true, + "doc_to_decontamination_query": "question" + }, + "truthfulqa_sk_mc2": { + "tag": [ + "truthfulqa_multilingual" + ], + "task": "truthfulqa_sk_mc2", + "repeats": 1, + "metadata": { + "version": 1 + }, + "description": "", + "doc_to_text": "query", + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "num_fewshot": 0, + "output_type": "multiple_choice", + "unsafe_code": false, + "dataset_name": "sk", + "dataset_path": "alexandrainst/m_truthfulqa", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc):\n out_doc = {\n \"question\": preprocess(doc[\"question\"]),\n \"query\": QA_PROMPT + \"\\n\\nQ: \" + preprocess(doc[\"question\"]) + \"\\nA:\",\n \"mc1_choices\": doc[\"mc1_targets_choices\"],\n \"mc2_choices\": doc[\"mc2_targets_choices\"],\n \"mc2_targets\": {\"labels\": doc[\"mc2_targets_labels\"]},\n \"gold\": \" \",\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", + "doc_to_choice": "mc2_choices", + "doc_to_target": 0, + "process_results": "def process_results_mc2(doc, results):\n lls, is_greedy = zip(*results)\n\n # Split on the first `0` as everything before it is true (`1`).\n split_idx = list(doc[\"mc2_targets\"][\"labels\"]).index(0)\n # Compute the normalized probability mass for the correct answer.\n ll_true, ll_false = lls[:split_idx], lls[split_idx:]\n p_true, p_false = np.exp(np.array(ll_true)), np.exp(np.array(ll_false))\n p_true = p_true / (sum(p_true) + sum(p_false))\n\n return {\"acc\": sum(p_true)}\n", + "target_delimiter": " ", + "validation_split": "val", + "fewshot_delimiter": "\n\n", + "should_decontaminate": true, + "doc_to_decontamination_query": "question" + } +} \ No newline at end of file diff --git a/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/truthfulqa_sk_mc1.yaml b/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/truthfulqa_sk_mc1.yaml new file mode 100644 index 0000000..92b1a45 --- /dev/null +++ b/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/truthfulqa_sk_mc1.yaml @@ -0,0 +1,34 @@ +tag: +- truthfulqa_multilingual +task: truthfulqa_sk_mc1 +repeats: 1 +metadata: + version: 1 +description: '' +doc_to_text: query +metric_list: +- metric: acc + aggregation: mean + higher_is_better: true +num_fewshot: 0 +output_type: multiple_choice +unsafe_code: false +dataset_name: sk +dataset_path: alexandrainst/m_truthfulqa +process_docs: "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n\ + \ def _process_doc(doc):\n out_doc = {\n \"question\": preprocess(doc[\"\ + question\"]),\n \"query\": QA_PROMPT + \"\\n\\nQ: \" + preprocess(doc[\"\ + question\"]) + \"\\nA:\",\n \"mc1_choices\": doc[\"mc1_targets_choices\"\ + ],\n \"mc2_choices\": doc[\"mc2_targets_choices\"],\n \"mc2_targets\"\ + : {\"labels\": doc[\"mc2_targets_labels\"]},\n \"gold\": \" \",\n \ + \ }\n return out_doc\n\n return dataset.map(_process_doc)\n" +doc_to_choice: mc1_choices +doc_to_target: 0 +target_delimiter: ' ' +validation_split: val +fewshot_delimiter: ' + + + ' +should_decontaminate: true +doc_to_decontamination_query: question diff --git a/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/truthfulqa_sk_mc2.yaml b/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/truthfulqa_sk_mc2.yaml new file mode 100644 index 0000000..6684fa7 --- /dev/null +++ b/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/truthfulqa_sk_mc2.yaml @@ -0,0 +1,40 @@ +tag: +- truthfulqa_multilingual +task: truthfulqa_sk_mc2 +repeats: 1 +metadata: + version: 1 +description: '' +doc_to_text: query +metric_list: +- metric: acc + aggregation: mean + higher_is_better: true +num_fewshot: 0 +output_type: multiple_choice +unsafe_code: false +dataset_name: sk +dataset_path: alexandrainst/m_truthfulqa +process_docs: "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n\ + \ def _process_doc(doc):\n out_doc = {\n \"question\": preprocess(doc[\"\ + question\"]),\n \"query\": QA_PROMPT + \"\\n\\nQ: \" + preprocess(doc[\"\ + question\"]) + \"\\nA:\",\n \"mc1_choices\": doc[\"mc1_targets_choices\"\ + ],\n \"mc2_choices\": doc[\"mc2_targets_choices\"],\n \"mc2_targets\"\ + : {\"labels\": doc[\"mc2_targets_labels\"]},\n \"gold\": \" \",\n \ + \ }\n return out_doc\n\n return dataset.map(_process_doc)\n" +doc_to_choice: mc2_choices +doc_to_target: 0 +process_results: "def process_results_mc2(doc, results):\n lls, is_greedy = zip(*results)\n\ + \n # Split on the first `0` as everything before it is true (`1`).\n split_idx\ + \ = list(doc[\"mc2_targets\"][\"labels\"]).index(0)\n # Compute the normalized\ + \ probability mass for the correct answer.\n ll_true, ll_false = lls[:split_idx],\ + \ lls[split_idx:]\n p_true, p_false = np.exp(np.array(ll_true)), np.exp(np.array(ll_false))\n\ + \ p_true = p_true / (sum(p_true) + sum(p_false))\n\n return {\"acc\": sum(p_true)}\n" +target_delimiter: ' ' +validation_split: val +fewshot_delimiter: ' + + + ' +should_decontaminate: true +doc_to_decontamination_query: question diff --git a/aya_300k_lr_experiments/benchmarks/lmeval/provenance/lm_eval_commit.txt b/aya_300k_lr_experiments/benchmarks/lmeval/provenance/lm_eval_commit.txt new file mode 100644 index 0000000..af55d49 --- /dev/null +++ b/aya_300k_lr_experiments/benchmarks/lmeval/provenance/lm_eval_commit.txt @@ -0,0 +1 @@ +6d2abda4fd171e68a8789330c4149e37c1ca0bda diff --git a/aya_300k_lr_experiments/benchmarks/lmeval/provenance/package_versions.json b/aya_300k_lr_experiments/benchmarks/lmeval/provenance/package_versions.json new file mode 100644 index 0000000..f351a5e --- /dev/null +++ b/aya_300k_lr_experiments/benchmarks/lmeval/provenance/package_versions.json @@ -0,0 +1,8 @@ +{ + "lm_eval": "0.4.8", + "transformers": "4.48.2", + "torch": "2.6.0+cu124", + "datasets": "5.0.1", + "peft": "0.14.0", + "evaluate": "0.4.6" +} diff --git a/aya_300k_lr_experiments/benchmarks/lmeval/provenance/run_settings.txt b/aya_300k_lr_experiments/benchmarks/lmeval/provenance/run_settings.txt new file mode 100644 index 0000000..47fe7cf --- /dev/null +++ b/aya_300k_lr_experiments/benchmarks/lmeval/provenance/run_settings.txt @@ -0,0 +1,8 @@ +model=hf +pretrained=/home/schwarc/diplomovka/skbench_evaluation/models/aya_300k_lr_0p00010 +tokenizer=/home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4 +dtype=bfloat16 +num_fewshot=0 +batch_size=1 +apply_chat_template=false +CUDA_VISIBLE_DEVICES=1 diff --git a/aya_300k_lr_experiments/benchmarks/lmeval/provenance/tasks.txt b/aya_300k_lr_experiments/benchmarks/lmeval/provenance/tasks.txt new file mode 100644 index 0000000..2ce65ea --- /dev/null +++ b/aya_300k_lr_experiments/benchmarks/lmeval/provenance/tasks.txt @@ -0,0 +1 @@ +arc_sk,hellaswag_sk,m_mmlu_sk,truthfulqa_sk_mc1,truthfulqa_sk_mc2,sklegal,skquad diff --git a/aya_300k_lr_experiments/benchmarks/lmeval/results/results_2026-10-24T02-28-32.987157.json b/aya_300k_lr_experiments/benchmarks/lmeval/results/results_2026-10-24T02-28-32.987157.json new file mode 100644 index 0000000..bd77cec --- /dev/null +++ b/aya_300k_lr_experiments/benchmarks/lmeval/results/results_2026-10-24T02-28-32.987157.json @@ -0,0 +1,431 @@ +{ + "results": { + "arc_sk": { + "alias": "arc_sk", + "acc,none": 0.30025662959794697, + "acc_stderr,none": 0.013412022629366669, + "acc_norm,none": 0.3473053892215569, + "acc_norm_stderr,none": 0.013931226499492353 + }, + "hellaswag_sk": { + "alias": "hellaswag_sk", + "acc,none": 0.4083289404322615, + "acc_stderr,none": 0.005047187883274763, + "acc_norm,none": 0.5163943068002108, + "acc_norm_stderr,none": 0.005131456510575215 + }, + "m_mmlu_sk": { + "alias": "m_mmlu_sk", + "acc,none": 0.3102893890675241, + "acc_stderr,none": 0.0040478899905589515 + }, + "sklegal": { + "alias": "sklegal", + "acc,none": 0.2856071964017991, + "acc_stderr,none": 0.012371946515772182, + "acc_norm,none": 0.46101949025487254, + "acc_norm_stderr,none": 0.013653094581189941 + }, + "skquad": { + "alias": "skquad", + "exact,none": 15.120525931336742, + "exact_stderr,none": "N/A", + "f1,none": 38.72920807013749, + "f1_stderr,none": "N/A", + "HasAns_exact,none": 18.540838569047313, + "HasAns_exact_stderr,none": "N/A", + "HasAns_f1,none": 47.549942420326644, + "HasAns_f1_stderr,none": "N/A", + "NoAns_exact,none": 0.1681614349775785, + "NoAns_exact_stderr,none": "N/A", + "NoAns_f1,none": 0.1681614349775785, + "NoAns_f1_stderr,none": "N/A", + "best_exact,none": 18.75195658979443, + "best_exact_stderr,none": "N/A", + "best_f1,none": 38.7292080701372, + "best_f1_stderr,none": "N/A" + }, + "truthfulqa_sk_mc1": { + "alias": "truthfulqa_sk_mc1", + "acc,none": 0.19665809768637532, + "acc_stderr,none": 0.014259215011433178 + }, + "truthfulqa_sk_mc2": { + "alias": "truthfulqa_sk_mc2", + "acc,none": 0.3506542164316134, + "acc_stderr,none": 0.014662940445997986 + } + }, + "group_subtasks": { + "arc_sk": [], + "hellaswag_sk": [], + "m_mmlu_sk": [], + "sklegal": [], + "skquad": [], + "truthfulqa_sk_mc1": [], + "truthfulqa_sk_mc2": [] + }, + "configs": { + "arc_sk": { + "task": "arc_sk", + "tag": [ + "arc_multilingual" + ], + "dataset_path": "alexandrainst/m_arc", + "dataset_name": "sk", + "training_split": "train", + "validation_split": "validation", + "test_split": "test", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc):\n # breakpoint()\n out_doc = {\n \"id\": doc[\"id\"],\n \"query\": \"Question: \" + preprocess(doc[\"instruction\"]) + \"\\nAnswer:\",\n \"choices\": [\n preprocess(option)\n for option in [\n doc[\"option_a\"],\n doc[\"option_b\"],\n doc[\"option_c\"],\n doc[\"option_d\"],\n doc[\"option_e\"],\n ]\n if option\n ],\n \"gold\": [\"A\", \"B\", \"C\", \"D\", \"E\"].index(doc[\"answer\"]),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", + "doc_to_text": "query", + "doc_to_target": "gold", + "unsafe_code": false, + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "acc_norm", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": true, + "doc_to_decontamination_query": "query", + "metadata": { + "version": 2.0 + } + }, + "hellaswag_sk": { + "task": "hellaswag_sk", + "tag": [ + "hellaswag_multilingual" + ], + "dataset_path": "alexandrainst/m_hellaswag", + "dataset_name": "sk", + "validation_split": "val", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc):\n ctx = doc[\"ctx_a\"] + \" \" + doc[\"ctx_b\"].capitalize()\n out_doc = {\n \"query\": preprocess(doc[\"activity_label\"] + \": \" + ctx),\n \"choices\": [preprocess(ending) for ending in doc[\"endings\"]],\n \"gold\": int(doc[\"label\"]),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", + "doc_to_text": "query", + "doc_to_target": "{{label.lstrip()}}", + "unsafe_code": false, + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "acc_norm", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 1.0 + } + }, + "m_mmlu_sk": { + "task": "m_mmlu_sk", + "tag": [ + "m_mmlu" + ], + "dataset_path": "alexandrainst/m_mmlu", + "dataset_name": "sk", + "test_split": "test", + "fewshot_split": "train", + "doc_to_text": "{{instruction.strip()}}\nA. {{option_a}}\nB. {{option_b}}\nC. {{option_c}}\nD. {{option_d}}\nAnswer:", + "doc_to_target": "answer", + "unsafe_code": false, + "doc_to_choice": [ + "A", + "B", + "C", + "D" + ], + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "fewshot_config": { + "sampler": "first_n" + }, + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + }, + "sklegal": { + "task": "sklegal", + "dataset_path": "TUKE-KEMT/slovak-legal-exam", + "test_split": "test", + "doc_to_text": "{{prompt}} ", + "doc_to_target": "label", + "unsafe_code": false, + "doc_to_choice": "{{answers}}", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "acc_norm", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 1 + } + }, + "skquad": { + "task": "skquad", + "unsafe_code": false, + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "output_type": "generate_until", + "generation_kwargs": { + "until": [ + "\n\n" + ], + "do_sample": false + }, + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 3 + } + }, + "truthfulqa_sk_mc1": { + "task": "truthfulqa_sk_mc1", + "tag": [ + "truthfulqa_multilingual" + ], + "dataset_path": "alexandrainst/m_truthfulqa", + "dataset_name": "sk", + "validation_split": "val", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc):\n out_doc = {\n \"question\": preprocess(doc[\"question\"]),\n \"query\": QA_PROMPT + \"\\n\\nQ: \" + preprocess(doc[\"question\"]) + \"\\nA:\",\n \"mc1_choices\": doc[\"mc1_targets_choices\"],\n \"mc2_choices\": doc[\"mc2_targets_choices\"],\n \"mc2_targets\": {\"labels\": doc[\"mc2_targets_labels\"]},\n \"gold\": \" \",\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", + "doc_to_text": "query", + "doc_to_target": 0, + "unsafe_code": false, + "doc_to_choice": "mc1_choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": true, + "doc_to_decontamination_query": "question", + "metadata": { + "version": 1.0 + } + }, + "truthfulqa_sk_mc2": { + "task": "truthfulqa_sk_mc2", + "tag": [ + "truthfulqa_multilingual" + ], + "dataset_path": "alexandrainst/m_truthfulqa", + "dataset_name": "sk", + "validation_split": "val", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc):\n out_doc = {\n \"question\": preprocess(doc[\"question\"]),\n \"query\": QA_PROMPT + \"\\n\\nQ: \" + preprocess(doc[\"question\"]) + \"\\nA:\",\n \"mc1_choices\": doc[\"mc1_targets_choices\"],\n \"mc2_choices\": doc[\"mc2_targets_choices\"],\n \"mc2_targets\": {\"labels\": doc[\"mc2_targets_labels\"]},\n \"gold\": \" \",\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", + "doc_to_text": "query", + "doc_to_target": 0, + "unsafe_code": false, + "doc_to_choice": "mc2_choices", + "process_results": "def process_results_mc2(doc, results):\n lls, is_greedy = zip(*results)\n\n # Split on the first `0` as everything before it is true (`1`).\n split_idx = list(doc[\"mc2_targets\"][\"labels\"]).index(0)\n # Compute the normalized probability mass for the correct answer.\n ll_true, ll_false = lls[:split_idx], lls[split_idx:]\n p_true, p_false = np.exp(np.array(ll_true)), np.exp(np.array(ll_false))\n p_true = p_true / (sum(p_true) + sum(p_false))\n\n return {\"acc\": sum(p_true)}\n", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": true, + "doc_to_decontamination_query": "question", + "metadata": { + "version": 1.0 + } + } + }, + "versions": { + "arc_sk": 2.0, + "hellaswag_sk": 1.0, + "m_mmlu_sk": 0.0, + "sklegal": 1, + "skquad": 3, + "truthfulqa_sk_mc1": 1.0, + "truthfulqa_sk_mc2": 1.0 + }, + "n-shot": { + "arc_sk": 0, + "hellaswag_sk": 0, + "m_mmlu_sk": 0, + "sklegal": 0, + "skquad": 0, + "truthfulqa_sk_mc1": 0, + "truthfulqa_sk_mc2": 0 + }, + "higher_is_better": { + "arc_sk": { + "acc": true, + "acc_norm": true + }, + "hellaswag_sk": { + "acc": true, + "acc_norm": true + }, + "m_mmlu_sk": { + "acc": true + }, + "sklegal": { + "acc": true, + "acc_norm": true + }, + "skquad": { + "exact": true, + "f1": true, + "HasAns_exact": true, + "HasAns_f1": true, + "NoAns_exact": true, + "NoAns_f1": true, + "best_exact": true, + "best_f1": true + }, + "truthfulqa_sk_mc1": { + "acc": true + }, + "truthfulqa_sk_mc2": { + "acc": true + } + }, + "n-samples": { + "truthfulqa_sk_mc2": { + "original": 778, + "effective": 778 + }, + "truthfulqa_sk_mc1": { + "original": 778, + "effective": 778 + }, + "skquad": { + "original": 9583, + "effective": 9583 + }, + "sklegal": { + "original": 1334, + "effective": 1334 + }, + "m_mmlu_sk": { + "original": 13062, + "effective": 13062 + }, + "hellaswag_sk": { + "original": 9485, + "effective": 9485 + }, + "arc_sk": { + "original": 1169, + "effective": 1169 + } + }, + "config": { + "model": "hf", + "model_args": "pretrained=/home/schwarc/diplomovka/skbench_evaluation/models/aya_300k_lr_0p00010,tokenizer=/home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4,dtype=bfloat16", + "model_num_parameters": 7241732096, + "model_dtype": "torch.bfloat16", + "model_revision": "main", + "model_sha": "", + "batch_size": "1", + "batch_sizes": [], + "device": "cuda:0", + "use_cache": null, + "limit": null, + "bootstrap_iters": 100000, + "gen_kwargs": null, + "random_seed": 0, + "numpy_seed": 1234, + "torch_seed": 1234, + "fewshot_seed": 1234 + }, + "git_hash": "6d2abda4", + "date": 1792795427.1860082, + "pretty_env_info": "PyTorch version: 2.6.0+cu124\nIs debug build: False\nCUDA used to build PyTorch: 12.4\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 24.04.5 LTS (x86_64)\nGCC version: (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0\nClang version: Could not collect\nCMake version: version 3.28.3\nLibc version: glibc-2.39\n\nPython version: 3.11.16 (main, Aug 27 2026, 14:44:21) [GCC 14.3.0] (64-bit runtime)\nPython platform: Linux-6.8.0-147-generic-x86_64-with-glibc2.39\nIs CUDA available: True\nCUDA runtime version: Could not collect\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA RTX A4500\nGPU 1: NVIDIA RTX A4500\n\nNvidia driver version: 580.178.04\ncuDNN version: Could not collect\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 16\nOn-line CPU(s) list: 0-15\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) W-2245 CPU @ 3.90GHz\nCPU family: 6\nModel: 85\nThread(s) per core: 2\nCore(s) per socket: 8\nSocket(s): 1\nStepping: 7\nCPU(s) scaling MHz: 49%\nCPU max MHz: 4700.0000\nCPU min MHz: 1200.0000\nBogoMIPS: 7799.87\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req vnmi avx512_vnni md_clear flush_l1d arch_capabilities ibpb_exit_to_user\nVirtualization: VT-x\nL1d cache: 256 KiB (8 instances)\nL1i cache: 256 KiB (8 instances)\nL2 cache: 8 MiB (8 instances)\nL3 cache: 16.5 MiB (1 instance)\nNUMA node(s): 1\nNUMA node0 CPU(s): 0-15\nVulnerability Gather data sampling: Mitigation; Microcode\nVulnerability Indirect target selection: Mitigation; Aligned branch/return thunks\nVulnerability Itlb multihit: KVM: Mitigation: VMX disabled\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Mitigation; Enhanced IBRS\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; PBRSB-eIBRS SW sequence; BHI SW loop, KVM SW loop\nVulnerability Srbds: Not affected\nVulnerability Tsa: Not affected\nVulnerability Tsx async abort: Mitigation; TSX disabled\nVulnerability Vmscape: Mitigation; IBPB before exit to userspace\n\nVersions of relevant libraries:\n[pip3] numpy==2.4.6\n[pip3] nvidia-cublas-cu12==12.4.5.8\n[pip3] nvidia-cuda-cupti-cu12==12.4.127\n[pip3] nvidia-cuda-nvrtc-cu12==12.4.127\n[pip3] nvidia-cuda-runtime-cu12==12.4.127\n[pip3] nvidia-cudnn-cu12==9.1.0.70\n[pip3] nvidia-cufft-cu12==11.2.1.3\n[pip3] nvidia-curand-cu12==10.3.5.147\n[pip3] nvidia-cusolver-cu12==11.6.1.9\n[pip3] nvidia-cusparse-cu12==12.3.1.170\n[pip3] nvidia-cusparselt-cu12==0.6.2\n[pip3] nvidia-nccl-cu12==2.21.5\n[pip3] nvidia-nvjitlink-cu12==12.4.127\n[pip3] nvidia-nvtx-cu12==12.4.127\n[pip3] torch==2.6.0+cu124\n[pip3] triton==3.2.0\n[conda] numpy 2.4.6 pypi_0 pypi\n[conda] nvidia-cublas-cu12 12.4.5.8 pypi_0 pypi\n[conda] nvidia-cuda-cupti-cu12 12.4.127 pypi_0 pypi\n[conda] nvidia-cuda-nvrtc-cu12 12.4.127 pypi_0 pypi\n[conda] nvidia-cuda-runtime-cu12 12.4.127 pypi_0 pypi\n[conda] nvidia-cudnn-cu12 9.1.0.70 pypi_0 pypi\n[conda] nvidia-cufft-cu12 11.2.1.3 pypi_0 pypi\n[conda] nvidia-curand-cu12 10.3.5.147 pypi_0 pypi\n[conda] nvidia-cusolver-cu12 11.6.1.9 pypi_0 pypi\n[conda] nvidia-cusparse-cu12 12.3.1.170 pypi_0 pypi\n[conda] nvidia-cusparselt-cu12 0.6.2 pypi_0 pypi\n[conda] nvidia-nccl-cu12 2.21.5 pypi_0 pypi\n[conda] nvidia-nvjitlink-cu12 12.4.127 pypi_0 pypi\n[conda] nvidia-nvtx-cu12 12.4.127 pypi_0 pypi\n[conda] torch 2.6.0+cu124 pypi_0 pypi\n[conda] triton 3.2.0 pypi_0 pypi", + "transformers_version": "4.48.2", + "upper_git_hash": null, + "tokenizer_pad_token": [ + "", + "2" + ], + "tokenizer_eos_token": [ + "", + "2" + ], + "tokenizer_bos_token": [ + "", + "1" + ], + "eot_token_id": 2, + "max_length": 32768, + "task_hashes": {}, + "model_source": "hf", + "model_name": "/home/schwarc/diplomovka/skbench_evaluation/models/aya_300k_lr_0p00010", + "model_name_sanitized": "__home__schwarc__diplomovka__skbench_evaluation__models__aya_300k_lr_0p00010", + "system_instruction": null, + "system_instruction_sha": null, + "fewshot_as_multiturn": false, + "chat_template": null, + "chat_template_sha": null, + "start_time": 896902.690480037, + "end_time": 910397.499522964, + "total_evaluation_time_seconds": "13494.809042926994" +} \ No newline at end of file diff --git a/aya_300k_lr_experiments/benchmarks/skbench/audit.md b/aya_300k_lr_experiments/benchmarks/skbench/audit.md new file mode 100644 index 0000000..4e3b0e9 --- /dev/null +++ b/aya_300k_lr_experiments/benchmarks/skbench/audit.md @@ -0,0 +1,82 @@ +# Audit úspešného čiastočného SK-bench behu + +Vybraný model po tréningu na 300k vzorke Aya s LR `0.00010` dokončil beh `sk_zero_available_without_nli_lr_0p00010_20261022_185255`. Log končí hláškou `Benchmarking completed.` a obsahuje výsledné metriky. Benchmarkovalo sa iba toto LR, vybrané podľa najnižšieho finálneho validačného loss zo šiestich tréningových behov; výsledky preto neporovnávajú benchmarkovú kvalitu šiestich LR ani budúceho tréningu na celej Aya množine. + +## Pokrytie a počty + +- Zoznam `configs/tasks.txt`, 222 konfigurácií úloh vo výslednom JSON a 222 súborov DETAILS sa zhodujú. Názvy sa pri porovnaní dekódujú z Windows `%3A`/`%7C` a normalizuje sa koncové označenie zero-shot variantu. +- Vyhodnotených bolo **222 zo 412 pôvodných task variantov**: 74 kombinácií úlohy/subtasku, každá s tromi promptmi. Ide o 13 rodín úloh. MMLU tvorí 171 variantov (57 predmetov × 3 prompty). +- DETAILS obsahujú **159 426 vyhodnotených inštancií**, po **53 142 na každý prompt**. Tri prompty opakovane hodnotia tie isté základné testové otázky; 159 426 nie je počet nezávislých otázok ani dôkaz unikátnosti medzi rôznymi datasetmi. +- `configs/omitted_tasks.txt` obsahuje 190 vynechaných variantov. Report dostupnosti eviduje 12 nedostupných repozitárov so 187 variantmi; tri ďalšie vynechané varianty patria samostatnému NLI-SK. Historický access report uvádza NLI-SK ako `OK`, ale jeho tri varianty v konečnom behu nie sú. NLI úlohy z rodiny SKLEP zostali zahrnuté. +- `max_samples: null` znamená, že beh nemal globálny limit vzoriek. Počty sa získali z metadát jednotlivých DETAILS súborov, nie zo zaokrúhlených metrík. + +Tento dostupný subset sa nesmie označovať za oficiálne kompletné SK-bench skóre. Chýbajúce úlohy ani chýbajúce rodiny sa neimputovali. + +## Zaznamenané nastavenie + +Výsledný JSON potvrdzuje `bfloat16`, kontext `32768`, tensor/data/pipeline parallelism `1/1/1`, využitie GPU pamäte `0.85`, seed modelu `1234`, teplotu `0` a `enforce_eager: true`. Pole `generation_parameters.seed` je `null`; konkrétny seed je uložený v `model_config.seed`. Všetky úlohy majú `num_fewshots: 0`. + +216 task variantov používa najviac 32 generovaných tokenov a šesť QA variantov 126 tokenov; stop sekvencia je nový riadok. Tieto limity výstupu treba pri porovnaní výsledkov zachovať. Záznam neobsahuje spoľahlivý počet odpovedí ukončených výstupným limitom. + +Zmrazený commit SK-bench je `15823bbcc2d995e69441fcb7d34abb61bd8180c5`. Pôvodné `lighteval_sha` vo výslednom JSON je `?`; tento údaj sa dodatočne nevymýšľa. `uv.lock`, commit, modelová konfigurácia, per-task konfigurácie a revízie datasetov sú uchované v `provenance/` a `configs/`. Log zaznamenáva aj runtime patchovanie; samotný commit a lock preto bez rovnakého postupu nie sú úplným dôkazom identického prostredia. + +## Orezanie dlhých vstupov + +Kontrola projektovala `model_response.truncated_tokens_count` zo **všetkých 222 DETAILS súborov**, bez čítania duplicitného generation cache. Všetkých 159 426 hodnôt existuje, žiadna nie je `null` a všetky sú nula. Aj pôvodné súhrnné počítadlá sú nulové vrátane `non_truncated`, takže samy osebe nepotvrdzujú neprítomnosť orezania. + +**Log priamo potvrdzuje orezanie kontextu.** Vyhľadanie `truncat` bez rozlíšenia veľkosti písmen našlo presne jednu hlášku, na LF riadku 16033: + +```text +context_size + max_new_tokens=53860 which is greater than self.max_length=32768. Truncating context to 32642 tokens. +``` + +Následná diagnostika dĺžok zo všetkých DETAILS našla **69 tokenových polí presne na vstupnom limite 32 642 tokenov**, po 23 v každom z troch variantov `multi_wiki_qa`. Vo všetkých troch sa opakujú tie isté indexy riadkov. Žiadne uložené pole neprekročilo príslušný limit `32768 − generation_size`; ostatné úlohy mali vstupy pod limitom. Súvisiace uložené reťazce vstupu mali pri hraničných riadkoch až 121 852 znakov. + +Týchto 69 riadkov sú **hraniční kandidáti zodpovedajúci orezaniu**, nie spoľahlivo dokázaný počet orezaných vstupov. Log neuvádza identifikátor konkrétneho tasku ani počet dotknutých vzoriek. Bez opätovnej tokenizácie pôvodných vstupov nemožno z dĺžky uloženého reťazca určiť pôvodnú dĺžku v tokenoch alebo počet odstránených tokenov. Overený záver je: **orezanie nastalo aspoň raz; jeho presný rozsah nie je z dostupných počítadiel známy**. + +Strojový súhrn preto obsahuje `context_truncation_observed_in_log: true`, `actual_truncated_sample_count: null` a nulové počítadlá označuje prefixom `details_reported_`. Podrobnosti sú v `results/truncation_audit_per_task.csv`, `results/log_truncation_evidence.json`, `results/input_length_diagnostics_per_task.csv` a `results/input_length_budget_boundary_rows.csv`. + +## Metriky a agregácia + +`results/per_task_metrics.csv` obsahuje iba 222 reálne vykonaných task variantov, ich skutočné metriky a štandardné chyby priamo z výsledného JSON. Prioritná metrika na čítanie je `em_relaxed`, prípadne `em_norm_relaxed`, ak ju daná úloha skutočne používa. QA má vlastné metriky `exact_match_slk_prefix` a `f1_slk`; nepremenúvajú sa na všeobecnú accuracy. + +Odvodené súhrny nikdy nemiešajú odlišné metriky do jedného skóre: + +- `family_summary.csv`: priemer v rodine, vždy osobitne pre konkrétnu metriku, cez všetky dostupné subtasky a tri prompty. +- `family_prompt_summary.csv`: tá istá agregácia osobitne pre prompt 1, 2 a 3. +- `three_prompt_summary.csv`: priemer troch promptov pre každú zo 74 konkrétnych kombinácií úlohy/subtasku, opäť pre každú metriku osobitne. +- `task_macro_mean` je aritmetický priemer task metrík s rovnakou váhou každého variantu. `sample_weighted_mean` váži tieto metriky počtom riadkov v DETAILS. Prompty sa v oboch prípadoch považujú za opakované vyhodnotenia, nie za nové nezávislé otázky. Súhrnné intervaly spoľahlivosti sa z per-task chýb nevymýšľajú. + +Pôvodný JSON má 226 kľúčov výsledkov: 222 úloh, tri vstavané MMLU promptové priemery a `all`. Štyri agregované riadky sú zachované v pôvodnom JSON, ale v odvodených súhrnoch sa znovu nepripočítavajú. `all` obsahuje viac metrík s rôznymi sadami úloh a netvorí oficiálne celkové skóre. + +Napríklad MMLU `em_relaxed` má task macro priemer `0.1600941253` a sample-weighted priemer `0.1572245190`. Rozdiel vzniká rôznou veľkosťou 57 predmetov. SKLEP klasifikačné úlohy s `em_norm_relaxed` majú macro `0.3507994510`, ale sample-weighted `0.2144044321`; výber váh tu zásadne mení opisný výsledok. + +## Obnovenie auditu + +Audit používa Python a `pyarrow==26.0.0`. Knižnica sa pri spracovaní nainštalovala len do pomocného priečinka pracovného projektu; nie je súčasťou tohto stromu. Pôvodné parquety, cache ani váhy sa do Git podkladov nekopírujú. Na opätovný audit treba samostatne rozbalený zdrojový archív a jeho `archive_manifest.json`. + +Archív treba najprv rozbaliť pomocou uloženého pomocného skriptu (Python 3.11+), ktorý vytvorí aj `archive_manifest.json` a `archive_summary.json`. Názvy s `:` a `|` prevedie na prenosné percent-encoded názvy a zachová mapovanie na originál. Priečinok určený cez `--output` musí byť nový alebo prázdny: + +```bash +python scripts/unpack_results_archive.py \ + --archive /path/to/aya_300k_benchmarky_20261024_215708.tar.gz \ + --output /path/to/extracted/archive +``` + +Z koreňa experimentu možno potom spustiť: + +```bash +python -m pip install --target .audit-deps -r benchmarks/skbench/provenance/requirements-audit.txt +python benchmarks/skbench/provenance/audit_skbench.py \ + --source /path/to/extracted/archive \ + --output benchmarks/skbench \ + --pyarrow-path .audit-deps +python benchmarks/skbench/provenance/input_length_diagnostics.py \ + --source /path/to/extracted/archive \ + --output benchmarks/skbench/results \ + --pyarrow-path .audit-deps +``` + +Prvý skript kontroluje pokrytie, kopíruje malé pôvodné artefakty, počíta metrikové súhrny a overuje truncation counter projekciou jediného stĺpca. Druhý skript projektuje uložené tokenové polia a vstupné reťazce, ale exportuje iba ich dĺžky a indexy hraničných riadkov. Texty otázok a odpovedí v týchto odvodených diagnostikách nie sú. + +SHA-256 presných kópií pôvodných súborov je v `provenance/copied_files_sha256.csv`; zdrojový archív identifikuje `provenance/archive_summary.json`. diff --git a/aya_300k_lr_experiments/benchmarks/skbench/configs/dataset_access_report.json b/aya_300k_lr_experiments/benchmarks/skbench/configs/dataset_access_report.json new file mode 100644 index 0000000..cc43959 --- /dev/null +++ b/aya_300k_lr_experiments/benchmarks/skbench/configs/dataset_access_report.json @@ -0,0 +1,622 @@ +[ + { + "repo": "CohereLabs/Global-MMLU-Lite", + "revision": "600af1bff2d447c7fc3aee22f11f607560d5fcb7", + "status": "OK", + "error": "", + "tasks": [ + "globalmmlu-lite.prompt3_mqa_single_correct_sk|0", + "globalmmlu-lite.prompt1_mqa_single_correct_sk|0", + "globalmmlu-lite.prompt2_mqa_single_correct_sk|0" + ] + }, + { + "repo": "NaiveNeuron/SlovakCOPA", + "revision": "5c61e4cd9343f9b4fab623bdc50bb895cc170771", + "status": "OK", + "error": "", + 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+scio.prompt2_mqa_single_correct_sk:vsp|0 +scio.prompt3_mqa_single_correct_sk:aj|0 +scio.prompt3_mqa_single_correct_sk:mat|0 +scio.prompt3_mqa_single_correct_sk:bio|0 +scio.prompt3_mqa_single_correct_sk:che|0 +scio.prompt3_mqa_single_correct_sk:vsp|0 +scio.prompt1_mqa_single_correct_sk:aj|0 +scio.prompt1_mqa_single_correct_sk:mat|0 +scio.prompt1_mqa_single_correct_sk:bio|0 +scio.prompt1_mqa_single_correct_sk:che|0 +scio.prompt1_mqa_single_correct_sk:vsp|0 diff --git a/aya_300k_lr_experiments/benchmarks/skbench/logs/sk_zero_available_without_nli_lr_0p00010_20261022_185255.log b/aya_300k_lr_experiments/benchmarks/skbench/logs/sk_zero_available_without_nli_lr_0p00010_20261022_185255.log new file mode 100644 index 0000000..05a6f64 --- /dev/null +++ b/aya_300k_lr_experiments/benchmarks/skbench/logs/sk_zero_available_without_nli_lr_0p00010_20261022_185255.log @@ -0,0 +1,31318 @@ +Successfully patched TokenizersBackend._patch_mistral_regex to default fix_mistral_regex=True. +Successfully patched VLLMModel._create_auto_tokenizer with litellm tokenizer. +Successfully added skip_special_tokens field to GenerationParameters. +Successfully added enforce_eager field to VLLMModelConfig (default=False). +Successfully patched VLLMModel and AsyncVLLMModel to respect config.enforce_eager. +Successfully patched PromptManager._prepare_chat_template. +arc.prompt3_mqa_single_correct_sk|0,arc.prompt1_mqa_single_correct_sk|0,arc.prompt2_mqa_single_correct_sk|0,belebele.prompt3_mqa_single_correct_sk|0,belebele.prompt1_mqa_single_correct_sk|0,belebele.prompt2_mqa_single_correct_sk|0,financial_exams.prompt1_completion_sk|0,financial_exams.prompt3_completion_sk|0,financial_exams.prompt2_completion_sk|0,mmlu.prompt3_mqa_single_correct_sk:abstract_algebra|0,mmlu.prompt1_mqa_single_correct_sk:abstract_algebra|0,mmlu.prompt2_mqa_single_correct_sk:abstract_algebra|0,mmlu.prompt3_mqa_single_correct_sk:anatomy|0,mmlu.prompt1_mqa_single_correct_sk:anatomy|0,mmlu.prompt2_mqa_single_correct_sk:anatomy|0,mmlu.prompt3_mqa_single_correct_sk:astronomy|0,mmlu.prompt1_mqa_single_correct_sk:astronomy|0,mmlu.prompt2_mqa_single_correct_sk:astronomy|0,mmlu.prompt3_mqa_single_correct_sk:business_ethics|0,mmlu.prompt1_mqa_single_correct_sk:business_ethics|0,mmlu.prompt2_mqa_single_correct_sk:business_ethics|0,mmlu.prompt3_mqa_single_correct_sk:clinical_knowledge|0,mmlu.prompt1_mqa_single_correct_sk:clinical_knowledge|0,mmlu.prompt2_mqa_single_correct_sk:clinical_knowledge|0,mmlu.prompt3_mqa_single_correct_sk:college_biology|0,mmlu.prompt1_mqa_single_correct_sk:college_biology|0,mmlu.prompt2_mqa_single_correct_sk:college_biology|0,mmlu.prompt3_mqa_single_correct_sk:college_chemistry|0,mmlu.prompt1_mqa_single_correct_sk:college_chemistry|0,mmlu.prompt2_mqa_single_correct_sk:college_chemistry|0,mmlu.prompt3_mqa_single_correct_sk:college_computer_science|0,mmlu.prompt1_mqa_single_correct_sk:college_computer_science|0,mmlu.prompt2_mqa_single_correct_sk:college_computer_science|0,mmlu.prompt3_mqa_single_correct_sk:college_mathematics|0,mmlu.prompt1_mqa_single_correct_sk:college_mathematics|0,mmlu.prompt2_mqa_single_correct_sk:college_mathematics|0,mmlu.prompt3_mqa_single_correct_sk:college_medicine|0,mmlu.prompt1_mqa_single_correct_sk:college_medicine|0,mmlu.prompt2_mqa_single_correct_sk:college_medicine|0,mmlu.prompt3_mqa_single_correct_sk:college_physics|0,mmlu.prompt1_mqa_single_correct_sk:college_physics|0,mmlu.prompt2_mqa_single_correct_sk:college_physics|0,mmlu.prompt3_mqa_single_correct_sk:computer_security|0,mmlu.prompt1_mqa_single_correct_sk:computer_security|0,mmlu.prompt2_mqa_single_correct_sk:computer_security|0,mmlu.prompt3_mqa_single_correct_sk:conceptual_physics|0,mmlu.prompt1_mqa_single_correct_sk:conceptual_physics|0,mmlu.prompt2_mqa_single_correct_sk:conceptual_physics|0,mmlu.prompt3_mqa_single_correct_sk:econometrics|0,mmlu.prompt1_mqa_single_correct_sk:econometrics|0,mmlu.prompt2_mqa_single_correct_sk:econometrics|0,mmlu.prompt3_mqa_single_correct_sk:electrical_engineering|0,mmlu.prompt1_mqa_single_correct_sk:electrical_engineering|0,mmlu.prompt2_mqa_single_correct_sk:electrical_engineering|0,mmlu.prompt3_mqa_single_correct_sk:elementary_mathematics|0,mmlu.prompt1_mqa_single_correct_sk:elementary_mathematics|0,mmlu.prompt2_mqa_single_correct_sk:elementary_mathematics|0,mmlu.prompt3_mqa_single_correct_sk:formal_logic|0,mmlu.prompt1_mqa_single_correct_sk:formal_logic|0,mmlu.prompt2_mqa_single_correct_sk:formal_logic|0,mmlu.prompt3_mqa_single_correct_sk:global_facts|0,mmlu.prompt1_mqa_single_correct_sk:global_facts|0,mmlu.prompt2_mqa_single_correct_sk:global_facts|0,mmlu.prompt3_mqa_single_correct_sk:high_school_biology|0,mmlu.prompt1_mqa_single_correct_sk:high_school_biology|0,mmlu.prompt2_mqa_single_correct_sk:high_school_biology|0,mmlu.prompt3_mqa_single_correct_sk:high_school_chemistry|0,mmlu.prompt1_mqa_single_correct_sk:high_school_chemistry|0,mmlu.prompt2_mqa_single_correct_sk:high_school_chemistry|0,mmlu.prompt3_mqa_single_correct_sk:high_school_computer_science|0,mmlu.prompt1_mqa_single_correct_sk:high_school_computer_science|0,mmlu.prompt2_mqa_single_correct_sk:high_school_computer_science|0,mmlu.prompt3_mqa_single_correct_sk:high_school_european_history|0,mmlu.prompt1_mqa_single_correct_sk:high_school_european_history|0,mmlu.prompt2_mqa_single_correct_sk:high_school_european_history|0,mmlu.prompt3_mqa_single_correct_sk:high_school_geography|0,mmlu.prompt1_mqa_single_correct_sk:high_school_geography|0,mmlu.prompt2_mqa_single_correct_sk:high_school_geography|0,mmlu.prompt3_mqa_single_correct_sk:high_school_government_and_politics|0,mmlu.prompt1_mqa_single_correct_sk:high_school_government_and_politics|0,mmlu.prompt2_mqa_single_correct_sk:high_school_government_and_politics|0,mmlu.prompt3_mqa_single_correct_sk:high_school_macroeconomics|0,mmlu.prompt1_mqa_single_correct_sk:high_school_macroeconomics|0,mmlu.prompt2_mqa_single_correct_sk:high_school_macroeconomics|0,mmlu.prompt3_mqa_single_correct_sk:high_school_mathematics|0,mmlu.prompt1_mqa_single_correct_sk:high_school_mathematics|0,mmlu.prompt2_mqa_single_correct_sk:high_school_mathematics|0,mmlu.prompt3_mqa_single_correct_sk:high_school_microeconomics|0,mmlu.prompt1_mqa_single_correct_sk:high_school_microeconomics|0,mmlu.prompt2_mqa_single_correct_sk:high_school_microeconomics|0,mmlu.prompt3_mqa_single_correct_sk:high_school_physics|0,mmlu.prompt1_mqa_single_correct_sk:high_school_physics|0,mmlu.prompt2_mqa_single_correct_sk:high_school_physics|0,mmlu.prompt3_mqa_single_correct_sk:high_school_psychology|0,mmlu.prompt1_mqa_single_correct_sk:high_school_psychology|0,mmlu.prompt2_mqa_single_correct_sk:high_school_psychology|0,mmlu.prompt3_mqa_single_correct_sk:high_school_statistics|0,mmlu.prompt1_mqa_single_correct_sk:high_school_statistics|0,mmlu.prompt2_mqa_single_correct_sk:high_school_statistics|0,mmlu.prompt3_mqa_single_correct_sk:high_school_us_history|0,mmlu.prompt1_mqa_single_correct_sk:high_school_us_history|0,mmlu.prompt2_mqa_single_correct_sk:high_school_us_history|0,mmlu.prompt3_mqa_single_correct_sk:high_school_world_history|0,mmlu.prompt1_mqa_single_correct_sk:high_school_world_history|0,mmlu.prompt2_mqa_single_correct_sk:high_school_world_history|0,mmlu.prompt3_mqa_single_correct_sk:human_aging|0,mmlu.prompt1_mqa_single_correct_sk:human_aging|0,mmlu.prompt2_mqa_single_correct_sk:human_aging|0,mmlu.prompt3_mqa_single_correct_sk:human_sexuality|0,mmlu.prompt1_mqa_single_correct_sk:human_sexuality|0,mmlu.prompt2_mqa_single_correct_sk:human_sexuality|0,mmlu.prompt3_mqa_single_correct_sk:international_law|0,mmlu.prompt1_mqa_single_correct_sk:international_law|0,mmlu.prompt2_mqa_single_correct_sk:international_law|0,mmlu.prompt3_mqa_single_correct_sk:jurisprudence|0,mmlu.prompt1_mqa_single_correct_sk:jurisprudence|0,mmlu.prompt2_mqa_single_correct_sk:jurisprudence|0,mmlu.prompt3_mqa_single_correct_sk:logical_fallacies|0,mmlu.prompt1_mqa_single_correct_sk:logical_fallacies|0,mmlu.prompt2_mqa_single_correct_sk:logical_fallacies|0,mmlu.prompt3_mqa_single_correct_sk:machine_learning|0,mmlu.prompt1_mqa_single_correct_sk:machine_learning|0,mmlu.prompt2_mqa_single_correct_sk:machine_learning|0,mmlu.prompt3_mqa_single_correct_sk:management|0,mmlu.prompt1_mqa_single_correct_sk:management|0,mmlu.prompt2_mqa_single_correct_sk:management|0,mmlu.prompt3_mqa_single_correct_sk:marketing|0,mmlu.prompt1_mqa_single_correct_sk:marketing|0,mmlu.prompt2_mqa_single_correct_sk:marketing|0,mmlu.prompt3_mqa_single_correct_sk:medical_genetics|0,mmlu.prompt1_mqa_single_correct_sk:medical_genetics|0,mmlu.prompt2_mqa_single_correct_sk:medical_genetics|0,mmlu.prompt3_mqa_single_correct_sk:miscellaneous|0,mmlu.prompt1_mqa_single_correct_sk:miscellaneous|0,mmlu.prompt2_mqa_single_correct_sk:miscellaneous|0,mmlu.prompt3_mqa_single_correct_sk:moral_disputes|0,mmlu.prompt1_mqa_single_correct_sk:moral_disputes|0,mmlu.prompt2_mqa_single_correct_sk:moral_disputes|0,mmlu.prompt3_mqa_single_correct_sk:moral_scenarios|0,mmlu.prompt1_mqa_single_correct_sk:moral_scenarios|0,mmlu.prompt2_mqa_single_correct_sk:moral_scenarios|0,mmlu.prompt3_mqa_single_correct_sk:nutrition|0,mmlu.prompt1_mqa_single_correct_sk:nutrition|0,mmlu.prompt2_mqa_single_correct_sk:nutrition|0,mmlu.prompt3_mqa_single_correct_sk:philosophy|0,mmlu.prompt1_mqa_single_correct_sk:philosophy|0,mmlu.prompt2_mqa_single_correct_sk:philosophy|0,mmlu.prompt3_mqa_single_correct_sk:prehistory|0,mmlu.prompt1_mqa_single_correct_sk:prehistory|0,mmlu.prompt2_mqa_single_correct_sk:prehistory|0,mmlu.prompt3_mqa_single_correct_sk:professional_accounting|0,mmlu.prompt1_mqa_single_correct_sk:professional_accounting|0,mmlu.prompt2_mqa_single_correct_sk:professional_accounting|0,mmlu.prompt3_mqa_single_correct_sk:professional_law|0,mmlu.prompt1_mqa_single_correct_sk:professional_law|0,mmlu.prompt2_mqa_single_correct_sk:professional_law|0,mmlu.prompt3_mqa_single_correct_sk:professional_medicine|0,mmlu.prompt1_mqa_single_correct_sk:professional_medicine|0,mmlu.prompt2_mqa_single_correct_sk:professional_medicine|0,mmlu.prompt3_mqa_single_correct_sk:professional_psychology|0,mmlu.prompt1_mqa_single_correct_sk:professional_psychology|0,mmlu.prompt2_mqa_single_correct_sk:professional_psychology|0,mmlu.prompt3_mqa_single_correct_sk:public_relations|0,mmlu.prompt1_mqa_single_correct_sk:public_relations|0,mmlu.prompt2_mqa_single_correct_sk:public_relations|0,mmlu.prompt3_mqa_single_correct_sk:security_studies|0,mmlu.prompt1_mqa_single_correct_sk:security_studies|0,mmlu.prompt2_mqa_single_correct_sk:security_studies|0,mmlu.prompt3_mqa_single_correct_sk:sociology|0,mmlu.prompt1_mqa_single_correct_sk:sociology|0,mmlu.prompt2_mqa_single_correct_sk:sociology|0,mmlu.prompt3_mqa_single_correct_sk:us_foreign_policy|0,mmlu.prompt1_mqa_single_correct_sk:us_foreign_policy|0,mmlu.prompt2_mqa_single_correct_sk:us_foreign_policy|0,mmlu.prompt3_mqa_single_correct_sk:virology|0,mmlu.prompt1_mqa_single_correct_sk:virology|0,mmlu.prompt2_mqa_single_correct_sk:virology|0,mmlu.prompt3_mqa_single_correct_sk:world_religions|0,mmlu.prompt1_mqa_single_correct_sk:world_religions|0,mmlu.prompt2_mqa_single_correct_sk:world_religions|0,multi_wiki_qa.prompt3_qa_sk|0,multi_wiki_qa.prompt2_qa_sk|0,multi_wiki_qa.prompt1_qa_sk|0,senti_sk.prompt2_sentiment_binary_sk|0,senti_sk.prompt3_sentiment_binary_sk|0,senti_sk.prompt1_sentiment_binary_sk|0,senti_sk.prompt3_sentiment_3_class_sk|0,senti_sk.prompt1_sentiment_3_class_sk|0,senti_sk.prompt2_sentiment_3_class_sk|0,skj1.prompt3_skj1_sk|0,skj1.prompt1_skj1_sk|0,skj1.prompt2_skj1_sk|0,sklep.prompt2_sentiment_binary_sk:sentiment-analysis|0,sklep.prompt3_sentiment_binary_sk:sentiment-analysis|0,sklep.prompt1_sentiment_binary_sk:sentiment-analysis|0,sklep.prompt1_hate_speech_sk:hate-speech|0,sklep.prompt3_hate_speech_sk:hate-speech|0,sklep.prompt2_hate_speech_sk:hate-speech|0,sklep.prompt1_nli_3_classes_sk:nli|0,sklep.prompt2_nli_3_classes_sk:nli|0,sklep.prompt3_nli_3_classes_sk:nli|0,sklep.prompt1_nli_binary_sk:rte|0,sklep.prompt3_nli_binary_sk:rte|0,sklep.prompt2_nli_binary_sk:rte|0,sklep.prompt3_qa_sk:question-answering|0,sklep.prompt2_qa_sk:question-answering|0,sklep.prompt1_qa_sk:question-answering|0,toxic_sk.prompt2_toxicity_sk|0,toxic_sk.prompt3_toxicity_sk|0,toxic_sk.prompt1_toxicity_sk|0,truthfulqa.prompt3_mqa_single_correct_sk:mc1|0,truthfulqa.prompt1_mqa_single_correct_sk:mc1|0,truthfulqa.prompt2_mqa_single_correct_sk:mc1|0,slovakcopa.prompt3_copa_sk|0,slovakcopa.prompt1_copa_sk|0,slovakcopa.prompt2_copa_sk|0,movie_review.prompt3_sentiment_binary_sk|0,movie_review.prompt2_sentiment_binary_sk|0,movie_review.prompt1_sentiment_binary_sk|0,globalmmlu-lite.prompt3_mqa_single_correct_sk|0,globalmmlu-lite.prompt1_mqa_single_correct_sk|0,globalmmlu-lite.prompt2_mqa_single_correct_sk|0/home/schwarc/diplomovka/sk-bench/.venv/lib/python3.13/site-packages/huggingface_hub/utils/_deprecation.py:100: FutureWarning: Deprecated argument(s) used in 'list_models': model_name. Will not be supported from version '2.0'. + +Use `search` instead. + warnings.warn(message, FutureWarning) +/home/schwarc/diplomovka/sk-bench/.venv/lib/python3.13/site-packages/huggingface_hub/utils/_deprecation.py:100: FutureWarning: Deprecated argument(s) used in 'list_models': model_name. Will not be supported from version '2.0'. + +Use `search` instead. + warnings.warn(message, FutureWarning) +Careful, the task financial_exams.prompt2_completion_sk is using evaluation data to build the few shot examples. +Careful, the task belebele.prompt3_mqa_single_correct_sk is using evaluation data to build the few shot examples. +Careful, the task belebele.prompt1_mqa_single_correct_sk is using evaluation data to build the few shot examples. +Careful, the task belebele.prompt2_mqa_single_correct_sk is using evaluation data to build the few shot examples. +Careful, the task skj1.prompt1_skj1_sk is using evaluation data to build the few shot examples. +Careful, the task financial_exams.prompt3_completion_sk is using evaluation data to build the few shot examples. +Careful, the task skj1.prompt2_skj1_sk is using evaluation data to build the few shot examples. +Careful, the task financial_exams.prompt1_completion_sk is using evaluation data to build the few shot examples. +Careful, the task skj1.prompt3_skj1_sk is using evaluation data to build the few shot examples. + +Applying chat template with reasoning effort: None + Filter: 0%| | 0/13062 [00:00= 0) for v in values) + row = {'task': task, 'sample_count': pf.metadata.num_rows, + 'truncation_values_present': sum(v is not None for v in values), + 'truncation_null_values': sum(v is None for v in values), + 'samples_with_truncation': sum(v is not None and v > 0 for v in values), + 'truncated_tokens_total': sum(v or 0 for v in values), + 'max_truncated_tokens_per_sample': max((v or 0 for v in values), default=0), + 'parquet_bytes': file.stat().st_size, + 'source_archive_member': original_paths.get(file.relative_to(source).as_posix(), file.relative_to(source).as_posix()), + 'extracted_relative_path': file.relative_to(source).as_posix()} + assert normalize(task) not in lookup + lookup[normalize(task)] = row + details.append(row) + assert set(lookup) == normalized_expected, 'DETAILS task coverage mismatch' + write_csv(out/'results/truncation_audit_per_task.csv', details) + + metric_names = sorted({key for task in configs for key in d['results'][task]}) + per_task = [] + for task in sorted(configs): + metric = d['results'][task] + config = configs[task] + detail = lookup[normalize(task)] + primary = next((name for name in ['em_relaxed', 'em_norm_relaxed', 'exact_match_slk_prefix'] if name in metric), None) + family = task.split('.prompt')[0] + prompt = int(re.search(r'\.prompt([123])_', task).group(1)) + task_group = re.sub(r'\.prompt[123]_', '.', task).removesuffix('|0') + row = {'task': task, 'family': family, 'task_group_without_prompt': task_group, + 'prompt': prompt, 'num_fewshots': config['num_fewshots'], 'sample_count': detail['sample_count'], + 'primary_metric': primary, 'primary_value': metric[primary], + 'generation_size': config['generation_size'], 'evaluation_splits': '|'.join(config['evaluation_splits']), + 'hf_repo': config['hf_repo'], 'hf_subset': config['hf_subset'], 'hf_revision': config['hf_revision']} + row.update({key: metric.get(key, '') for key in metric_names}) + per_task.append(row) + write_csv(out/'results/per_task_metrics.csv', per_task) + + # Each summary groups only identical metrics; values remain 0..1. + # Macro means weight task variants equally. Sample-weighted means weight + # their actual DETAILS rows. Repeated prompts are evaluation instances, + # never additional independent underlying test questions. + def summarize(group_columns, filename): + groups = collections.defaultdict(list) + for row in per_task: + for metric_name in metric_names: + if not metric_name.endswith('_stderr') and row[metric_name] != '': + groups[tuple(row[x] for x in group_columns)+(metric_name,)].append(row) + result = [] + for key, rows in sorted(groups.items()): + metric_name = key[-1] + total = sum(x['sample_count'] for x in rows) + item = dict(zip(group_columns, key[:-1])) + item.update({'metric': metric_name, 'task_variants': len(rows), + 'prompt_count': len({x['prompt'] for x in rows}), + 'evaluated_instances': total, + 'task_macro_mean': statistics.mean(x[metric_name] for x in rows), + 'sample_weighted_mean': sum(x['sample_count']*x[metric_name] for x in rows)/total, + 'min_task_value': min(x[metric_name] for x in rows), + 'max_task_value': max(x[metric_name] for x in rows)}) + result.append(item) + write_csv(out/'results'/filename, result) + return result + family_summary = summarize(['family'], 'family_summary.csv') + summarize(['family', 'prompt'], 'family_prompt_summary.csv') + three_prompt = summarize(['family', 'task_group_without_prompt'], 'three_prompt_summary.csv') + assert all(x['task_variants'] == x['prompt_count'] == 3 for x in three_prompt) + + access = json.loads((base/'configs/dataset_access_report.json').read_text(encoding='utf-8')) + inaccessible = [x for x in access if x['status'] != 'OK'] + with (base/'logs'/f'{run_name}.log').open(encoding='utf-8', newline='') as stream: + log_text = stream.read() + truncation_warnings = re.findall(r'context_size \+ max_new_tokens=\d+[^\r\n]*Truncating context to \d+ tokens\.', log_text) + warning_evidence = [] + for line_number, line in enumerate(log_text.split('\n'), 1): + if 'Truncating context' in line: + warning_evidence.append({'line_number': line_number, 'message': line[line.index('context_size'):], 'source': f'logs/{run_name}.log'}) + (out/'results/log_truncation_evidence.json').write_text(json.dumps(warning_evidence, indent=2, ensure_ascii=False)+'\n', encoding='utf-8') + summary = {'run_name': run_name, 'result_file': result_path.name, + 'task_list_count': len(tasks), 'configured_tasks': len(configs), 'scored_task_variants': len(per_task), + 'result_json_keys': len(d['results']), + 'extra_result_keys_excluded_from_task_summaries': sorted(set(d['results'])-set(configs)), + 'all_original_task_variants': len(all_tasks), + 'omitted_task_variants': len(omitted), 'inaccessible_repositories': len(inaccessible), + 'inaccessible_task_variants': sum(len(x['tasks']) for x in inaccessible), + 'additionally_excluded_nli_task_variants': 3, + 'families': dict(sorted(collections.Counter(row['family'] for row in per_task).items())), + 'prompt_counts': dict(sorted(collections.Counter(row['prompt'] for row in per_task).items())), + 'evaluated_instances': sum(row['sample_count'] for row in per_task), + 'instances_by_prompt': {str(prompt): sum(x['sample_count'] for x in per_task if x['prompt'] == prompt) for prompt in [1,2,3]}, + 'details_files_audited': len(details), 'truncation_column': 'model_response.truncated_tokens_count', + 'truncation_values_present': sum(x['truncation_values_present'] for x in details), + 'truncation_null_values': sum(x['truncation_null_values'] for x in details), + 'details_reported_samples_with_truncation': sum(x['samples_with_truncation'] for x in details), + 'details_reported_truncated_tokens_total': sum(x['truncated_tokens_total'] for x in details), + 'details_reported_max_truncated_tokens_per_sample': max(x['max_truncated_tokens_per_sample'] for x in details), + 'context_truncation_observed_in_log': bool(truncation_warnings), + 'actual_truncated_sample_count': None, + 'console_context_truncation_warning_count': len(truncation_warnings), + 'console_context_truncation_warnings': truncation_warnings, + 'truncation_interpretation': 'All DETAILS counters are zero, but the console explicitly reports context truncation. Do not infer no truncation or an affected-sample count from these counters.', + 'model_config': d['config_general']['model_config'], + 'max_samples': d['config_general']['max_samples'], + 'generation_size_counts': dict(collections.Counter(str(x['generation_size']) for x in per_task)), + 'original_summary_general': d['summary_general'], + 'pyarrow_version': pyarrow.__version__, 'python_version': sys.version, + 'aggregation': 'Descriptive metric-specific task macro means and sample-weighted means; no official full-SK-bench overall score.', + 'limitations': ['Partial available zero-shot subset: 222 of 412 task variants.', + 'Only the validation-selected LR=0.00010 was benchmarked.', + 'Sample counts are evaluation instances; prompt variants reuse underlying questions.', + 'One console warning reports context truncation; all DETAILS counters are zero, so the recorded counters do not reliably enumerate affected inputs.', + 'The audit cannot recover the number of affected tasks/samples or quantify output generation-size termination.', + 'All-zero summary counters include non_truncated=0 and cannot prove absence of truncation.', + 'Raw DETAILS and generation-cache parquets are excluded from this curated tree.']} + (out/'results/audit_summary.json').write_text(json.dumps(summary, indent=2, ensure_ascii=False)+'\n', encoding='utf-8') + script_destination = out/'provenance/audit_skbench.py' + if pathlib.Path(__file__).resolve() != script_destination.resolve(): + shutil.copyfile(pathlib.Path(__file__), script_destination) + print(json.dumps({key: summary[key] for key in ['scored_task_variants', 'evaluated_instances', 'instances_by_prompt', 'details_reported_samples_with_truncation', 'context_truncation_observed_in_log', 'actual_truncated_sample_count', 'truncation_null_values', 'families']}, indent=2)) + print('Primary family macro means (metrics kept separate):') + for row in family_summary: + if row['metric'] in ['em_relaxed', 'em_norm_relaxed', 'exact_match_slk_prefix', 'f1_slk']: + print(row) + + +if __name__ == '__main__': + main() diff --git a/aya_300k_lr_experiments/benchmarks/skbench/provenance/copied_files_sha256.csv b/aya_300k_lr_experiments/benchmarks/skbench/provenance/copied_files_sha256.csv new file mode 100644 index 0000000..8a6bbd7 --- /dev/null +++ b/aya_300k_lr_experiments/benchmarks/skbench/provenance/copied_files_sha256.csv @@ -0,0 +1,14 @@ +source_archive_member,curated_path,bytes,sha256 +skbench_evaluation/results/sk_zero_available_without_nli_lr_0p00010_20261022_185255/results/home/schwarc/diplomovka/skbench_evaluation/models/aya_300k_lr_0p00010/results_2026-10-22T21-55-04.268099.json,results/results_2026-10-22T21-55-04.268099.json,638180,966123dc20b547860d04f3434f65752c681ca2d83baebee22873e553161ff0df +skbench_evaluation/logs/sk_zero_available_without_nli_lr_0p00010_20261022_185255.log,logs/sk_zero_available_without_nli_lr_0p00010_20261022_185255.log,4537535,23a857210201913c5506ac39553a86fd803ed0c057e1226fcd978030a40b6c78 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+skbench_evaluation/configs/dataset_access_report.json,configs/dataset_access_report.json,29828,8eea8c082d1eb914850128d48021a39c7e1fb7189578c5f1e496004af6ff593b +skbench_evaluation/configs/export_lr_0p00010.yaml,configs/export_lr_0p00010.yaml,391,bff3e0b8abd130122c8d88be71647e0ea67e4a836b3caaed4e3bc307ef267dca +skbench_evaluation/configs/tasks_sk_zero.txt,configs/tasks_sk_zero.txt,20044,25c3cdfeb1f46adb86b68be24a9d8e22439733501e2fffda1ce5034e610660a4 +skbench_evaluation/configs/tasks_sk_zero_available.txt,configs/tasks_sk_zero_available.txt,11517,5ce7f1666b2073afd93aab03a116937f48954cf51d313c567c198b31f0dfcaf2 +skbench_evaluation/configs/tasks_sk_zero_available_without_nli.txt,configs/tasks_sk_zero_available_without_nli.txt,11415,297c7185d8ddea6899d20287ed5cffb895116cf756121252a0e76070b248db76 +skbench_evaluation/configs/tasks_sk_zero_unavailable.txt,configs/tasks_sk_zero_unavailable.txt,8527,0071cffec8ab2446a5594d0b3a3116151e7fc375bb300394e9ea7cb91ed7df10 diff --git a/aya_300k_lr_experiments/benchmarks/skbench/provenance/input_length_diagnostics.py b/aya_300k_lr_experiments/benchmarks/skbench/provenance/input_length_diagnostics.py new file mode 100644 index 0000000..b95f6eb --- /dev/null +++ b/aya_300k_lr_experiments/benchmarks/skbench/provenance/input_length_diagnostics.py @@ -0,0 +1,62 @@ +"""Inspect token-array and raw-prompt lengths; never export prompt content.""" +import argparse, csv, json, pathlib, re, shutil, statistics, sys +from urllib.parse import unquote + +parser=argparse.ArgumentParser(description=__doc__) +parser.add_argument('--source',type=pathlib.Path,required=True) +parser.add_argument('--output',type=pathlib.Path,required=True) +parser.add_argument('--pyarrow-path',type=pathlib.Path) +args=parser.parse_args() +if args.pyarrow_path: sys.path.insert(0,str(args.pyarrow_path.resolve())) +import pyarrow.parquet as pq +run=args.source/'skbench_evaluation/results/sk_zero_available_without_nli_lr_0p00010_20261022_185255' +d=json.loads(next((run/'results').rglob('*.json')).read_text()) +limit=d['config_general']['model_config']['max_model_length'] +rows=[] +outliers=[] +for file in sorted((run/'details').rglob('*.parquet')): + task=re.match(r'details_(.+)_2026-10-22T21-55-04\.268099\.parquet',unquote(file.name)).group(1) + cfg=d['config_tasks'][task] + budget=limit-cfg['generation_size'] + data=pq.ParquetFile(file).read(columns=['model_response.input_tokens','model_response.input']) + data=data.column('model_response').combine_chunks() + tokens=data.field('input_tokens').to_pylist() + texts=data.field('input').to_pylist() + lens=[None if value is None else len(value) for value in tokens] + chars=[None if value is None else len(value) for value in texts] + row={'task':task,'generation_size':cfg['generation_size'],'context_limit':limit,'input_budget':budget, + 'samples':len(lens),'null_token_arrays':sum(x is None for x in lens),'null_input_strings':sum(x is None for x in chars), + 'min_recorded_input_tokens':min(x for x in lens if x is not None), + 'max_recorded_input_tokens':max(x for x in lens if x is not None), + 'mean_recorded_input_tokens':statistics.mean(x for x in lens if x is not None), + 'arrays_at_input_budget':sum(x==budget for x in lens), + 'arrays_over_input_budget':sum(x is not None and x>budget for x in lens), + 'max_recorded_input_characters':max(x for x in chars if x is not None)} + rows.append(row) + for index,length in enumerate(lens): + if length is not None and length>=budget: + outliers.append({'task':task,'details_row_index_zero_based':index,'input_token_array_length':length, + 'generation_size':cfg['generation_size'],'input_budget':budget, + 'recorded_input_characters':chars[index]}) +for name,values in [('input_length_diagnostics_per_task.csv',rows),('input_length_budget_boundary_rows.csv',outliers)]: + with (args.output/name).open('w',newline='',encoding='utf-8') as f: + writer=csv.DictWriter(f,fieldnames=list(values[0]) if values else ['task']) + writer.writeheader();writer.writerows(values) +with (args.source/'skbench_evaluation/logs/sk_zero_available_without_nli_lr_0p00010_20261022_185255.log').open(encoding='utf-8',newline='') as stream: + log=stream.read() +messages=[] +for number,line in enumerate(log.split('\n'),1): + if re.search(r'truncat',line,re.I): + messages.append({'log_lf_line_number':number,'message':re.sub(r'\x1b\[[0-9;]*[A-Za-z]','',line).strip()[-1500:]}) +summary={'task_count':len(rows),'evaluated_instances':sum(x['samples'] for x in rows), + 'max_recorded_input_tokens':max(x['max_recorded_input_tokens'] for x in rows), + 'arrays_at_input_budget':sum(x['arrays_at_input_budget'] for x in rows), + 'arrays_over_input_budget':sum(x['arrays_over_input_budget'] for x in rows), + 'log_messages_matching_truncat_case_insensitive':messages, + 'limitation':'Input strings and token arrays are recorded artifacts, not a separately tokenized original source prompt. Their lengths cannot recover how much text was discarded or establish the number of truly truncated inputs.'} +(args.output/'input_length_diagnostics_summary.json').write_text(json.dumps(summary,indent=2,ensure_ascii=False)+'\n',encoding='utf-8') +print(json.dumps(summary,indent=2,ensure_ascii=False)) +print('BOUNDARY TASK COUNTS',json.dumps({task:sum(row['task']==task for row in outliers) for task in sorted({row['task'] for row in outliers})})) +script_destination=args.output.parent/'provenance/input_length_diagnostics.py' +if pathlib.Path(__file__).resolve()!=script_destination.resolve(): + shutil.copyfile(pathlib.Path(__file__),script_destination) diff --git a/aya_300k_lr_experiments/benchmarks/skbench/provenance/requirements-audit.txt b/aya_300k_lr_experiments/benchmarks/skbench/provenance/requirements-audit.txt new file mode 100644 index 0000000..8ed4982 --- /dev/null +++ b/aya_300k_lr_experiments/benchmarks/skbench/provenance/requirements-audit.txt @@ -0,0 +1 @@ +pyarrow==26.0.0 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4491.94s/it]context_size + max_new_tokens=53860 which is greater than self.max_length=32768. Truncating context to 32642 tokens." + } + ], + "limitation": "Input strings and token arrays are recorded artifacts, not a separately tokenized original source prompt. Their lengths cannot recover how much text was discarded or establish the number of truly truncated inputs." +} diff --git a/aya_300k_lr_experiments/benchmarks/skbench/results/log_truncation_evidence.json b/aya_300k_lr_experiments/benchmarks/skbench/results/log_truncation_evidence.json new file mode 100644 index 0000000..ed8e228 --- /dev/null +++ b/aya_300k_lr_experiments/benchmarks/skbench/results/log_truncation_evidence.json @@ -0,0 +1,7 @@ +[ + { + "line_number": 16033, + "message": "context_size + max_new_tokens=53860 which is greater than self.max_length=32768. Truncating context to 32642 tokens.", + "source": "logs/sk_zero_available_without_nli_lr_0p00010_20261022_185255.log" + } +] diff --git a/aya_300k_lr_experiments/benchmarks/skbench/results/per_task_metrics.csv b/aya_300k_lr_experiments/benchmarks/skbench/results/per_task_metrics.csv new file mode 100644 index 0000000..b9fa624 --- /dev/null +++ b/aya_300k_lr_experiments/benchmarks/skbench/results/per_task_metrics.csv @@ -0,0 +1,223 @@ +task,family,task_group_without_prompt,prompt,num_fewshots,sample_count,primary_metric,primary_value,generation_size,evaluation_splits,hf_repo,hf_subset,hf_revision,em,em_conservative,em_conservative_norm,em_conservative_norm_stderr,em_conservative_stderr,em_norm,em_norm_relaxed,em_norm_relaxed_stderr,em_norm_stderr,em_relaxed,em_relaxed_stderr,em_stderr,exact_match_slk_prefix,exact_match_slk_prefix_stderr,f1_slk,f1_slk_stderr 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+dataset_dir: data +dataset: aya_slovak_300k_fixed +template: alpaca +cutoff_len: 1024 +packing: false +train_on_prompt: false +finetuning_type: lora +lora_rank: 16 +lora_alpha: 32 +lora_dropout: 0.0 +lora_target: q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj +per_device_train_batch_size: 1 +per_device_eval_batch_size: 1 +gradient_accumulation_steps: 16 +learning_rate: 8.0e-05 +lr_scheduler_type: cosine +warmup_steps: 1500 +max_grad_norm: 0.5 +bf16: true +fp16: false +optim: adamw_torch +flash_attn: auto +plot_loss: true +ddp_timeout: 180000000 +ddp_find_unused_parameters: false +include_num_input_tokens_seen: true +enable_thinking: false +use_unsloth: true +use_unsloth_gc: true +streaming: false +eval_strategy: steps +val_size: 1000 +eval_steps: 1000 +overwrite_cache: false +overwrite_output_dir: false +output_dir: /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00008 +logging_steps: 10 +save_steps: 2000 +save_total_limit: 2 +report_to: none +seed: 42 +accelerator_config: + dispatch_batches: false + split_batches: false +num_train_epochs: 1.0 diff --git a/aya_300k_lr_experiments/configs/lr_0p00010.yaml b/aya_300k_lr_experiments/configs/lr_0p00010.yaml new file mode 100644 index 0000000..a93e7d4 --- /dev/null +++ b/aya_300k_lr_experiments/configs/lr_0p00010.yaml @@ -0,0 +1,50 @@ +stage: sft +do_train: true +do_eval: true +model_name_or_path: slovak-nlp/mistral-sk-7b +trust_remote_code: true +dataset_dir: data +dataset: aya_slovak_300k_fixed +template: alpaca +cutoff_len: 1024 +packing: false +train_on_prompt: false +finetuning_type: lora +lora_rank: 16 +lora_alpha: 32 +lora_dropout: 0.0 +lora_target: q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj +per_device_train_batch_size: 1 +per_device_eval_batch_size: 1 +gradient_accumulation_steps: 16 +learning_rate: 0.0001 +lr_scheduler_type: cosine +warmup_steps: 1500 +max_grad_norm: 0.5 +bf16: true +fp16: false +optim: adamw_torch +flash_attn: auto +plot_loss: true +ddp_timeout: 180000000 +ddp_find_unused_parameters: false +include_num_input_tokens_seen: true +enable_thinking: false +use_unsloth: true +use_unsloth_gc: true +streaming: false +eval_strategy: steps +val_size: 1000 +eval_steps: 1000 +overwrite_cache: false +overwrite_output_dir: false +output_dir: /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00010 +logging_steps: 10 +save_steps: 2000 +save_total_limit: 2 +report_to: none +seed: 42 +accelerator_config: + dispatch_batches: false + split_batches: false +num_train_epochs: 1.0 diff --git a/aya_300k_lr_experiments/configs/lr_0p00015.yaml b/aya_300k_lr_experiments/configs/lr_0p00015.yaml new file mode 100644 index 0000000..a96cf77 --- /dev/null +++ b/aya_300k_lr_experiments/configs/lr_0p00015.yaml @@ -0,0 +1,50 @@ +stage: sft +do_train: true +do_eval: true +model_name_or_path: slovak-nlp/mistral-sk-7b +trust_remote_code: true +dataset_dir: data +dataset: aya_slovak_300k_fixed +template: alpaca +cutoff_len: 1024 +packing: false +train_on_prompt: false +finetuning_type: lora +lora_rank: 16 +lora_alpha: 32 +lora_dropout: 0.0 +lora_target: q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj +per_device_train_batch_size: 1 +per_device_eval_batch_size: 1 +gradient_accumulation_steps: 16 +learning_rate: 0.00015 +lr_scheduler_type: cosine +warmup_steps: 1500 +max_grad_norm: 0.5 +bf16: true +fp16: false +optim: adamw_torch +flash_attn: auto +plot_loss: true +ddp_timeout: 180000000 +ddp_find_unused_parameters: false +include_num_input_tokens_seen: true +enable_thinking: false +use_unsloth: true +use_unsloth_gc: true +streaming: false +eval_strategy: steps +val_size: 1000 +eval_steps: 1000 +overwrite_cache: false +overwrite_output_dir: false +output_dir: /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00015 +logging_steps: 10 +save_steps: 2000 +save_total_limit: 2 +report_to: none +seed: 42 +accelerator_config: + dispatch_batches: false + split_batches: false +num_train_epochs: 1.0 diff --git a/aya_300k_lr_experiments/configs/lr_0p00018.yaml b/aya_300k_lr_experiments/configs/lr_0p00018.yaml new file mode 100644 index 0000000..eaaf3c5 --- /dev/null +++ b/aya_300k_lr_experiments/configs/lr_0p00018.yaml @@ -0,0 +1,50 @@ +stage: sft +do_train: true +do_eval: true +model_name_or_path: slovak-nlp/mistral-sk-7b +trust_remote_code: true +dataset_dir: data +dataset: aya_slovak_300k_fixed +template: alpaca +cutoff_len: 1024 +packing: false +train_on_prompt: false +finetuning_type: lora +lora_rank: 16 +lora_alpha: 32 +lora_dropout: 0.0 +lora_target: q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj +per_device_train_batch_size: 1 +per_device_eval_batch_size: 1 +gradient_accumulation_steps: 16 +learning_rate: 0.00018 +lr_scheduler_type: cosine +warmup_steps: 1500 +max_grad_norm: 0.5 +bf16: true +fp16: false +optim: adamw_torch +flash_attn: auto +plot_loss: true +ddp_timeout: 180000000 +ddp_find_unused_parameters: false +include_num_input_tokens_seen: true +enable_thinking: false +use_unsloth: true +use_unsloth_gc: true +streaming: false +eval_strategy: steps +val_size: 1000 +eval_steps: 1000 +overwrite_cache: false +overwrite_output_dir: true +output_dir: /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00018 +logging_steps: 10 +save_steps: 2000 +save_total_limit: 2 +report_to: none +seed: 42 +accelerator_config: + dispatch_batches: false + split_batches: false +num_train_epochs: 1.0 diff --git a/aya_300k_lr_experiments/configs/lr_0p00020.yaml b/aya_300k_lr_experiments/configs/lr_0p00020.yaml new file mode 100644 index 0000000..a646c27 --- /dev/null +++ b/aya_300k_lr_experiments/configs/lr_0p00020.yaml @@ -0,0 +1,50 @@ +stage: sft +do_train: true +do_eval: true +model_name_or_path: slovak-nlp/mistral-sk-7b +trust_remote_code: true +dataset_dir: data +dataset: aya_slovak_300k_fixed +template: alpaca +cutoff_len: 1024 +packing: false +train_on_prompt: false +finetuning_type: lora +lora_rank: 16 +lora_alpha: 32 +lora_dropout: 0.0 +lora_target: q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj +per_device_train_batch_size: 1 +per_device_eval_batch_size: 1 +gradient_accumulation_steps: 16 +learning_rate: 0.0002 +lr_scheduler_type: cosine +warmup_steps: 1500 +max_grad_norm: 0.5 +bf16: true +fp16: false +optim: adamw_torch +flash_attn: auto +plot_loss: true +ddp_timeout: 180000000 +ddp_find_unused_parameters: false +include_num_input_tokens_seen: true +enable_thinking: false +use_unsloth: true +use_unsloth_gc: true +streaming: false +eval_strategy: steps +val_size: 1000 +eval_steps: 1000 +overwrite_cache: false +overwrite_output_dir: true +output_dir: /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00020 +logging_steps: 10 +save_steps: 2000 +save_total_limit: 2 +report_to: none +seed: 42 +accelerator_config: + dispatch_batches: false + split_batches: false +num_train_epochs: 1.0 diff --git a/aya_300k_lr_experiments/configs/lr_0p00023.yaml b/aya_300k_lr_experiments/configs/lr_0p00023.yaml new file mode 100644 index 0000000..491637a --- /dev/null +++ b/aya_300k_lr_experiments/configs/lr_0p00023.yaml @@ -0,0 +1,50 @@ +stage: sft +do_train: true +do_eval: true +model_name_or_path: slovak-nlp/mistral-sk-7b +trust_remote_code: true +dataset_dir: data +dataset: aya_slovak_300k_fixed +template: alpaca +cutoff_len: 1024 +packing: false +train_on_prompt: false +finetuning_type: lora +lora_rank: 16 +lora_alpha: 32 +lora_dropout: 0.0 +lora_target: q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj +per_device_train_batch_size: 1 +per_device_eval_batch_size: 1 +gradient_accumulation_steps: 16 +learning_rate: 0.00023 +lr_scheduler_type: cosine +warmup_steps: 1500 +max_grad_norm: 0.5 +bf16: true +fp16: false +optim: adamw_torch +flash_attn: auto +plot_loss: true +ddp_timeout: 180000000 +ddp_find_unused_parameters: false +include_num_input_tokens_seen: true +enable_thinking: false +use_unsloth: true +use_unsloth_gc: true +streaming: false +eval_strategy: steps +val_size: 1000 +eval_steps: 1000 +overwrite_cache: false +overwrite_output_dir: true +output_dir: /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00023 +logging_steps: 10 +save_steps: 2000 +save_total_limit: 2 +report_to: none +seed: 42 +accelerator_config: + dispatch_batches: false + split_batches: false +num_train_epochs: 1.0 diff --git a/aya_300k_lr_experiments/logs/lr_0p00008.log b/aya_300k_lr_experiments/logs/lr_0p00008.log new file mode 100644 index 0000000..6f78dc4 --- /dev/null +++ b/aya_300k_lr_experiments/logs/lr_0p00008.log @@ -0,0 +1,12451 @@ +[INFO|2026-10-21 19:11:32] llamafactory.hparams.parser:651 >> Process rank: 0, world size: 1, device: cuda:0, distributed training: False, compute dtype: torch.bfloat16 +Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads. +[INFO|configuration_utils.py:765] 2026-10-21 19:11:32,966 >> loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +[INFO|configuration_utils.py:841] 2026-10-21 19:11:32,975 >> Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +[INFO|configuration_utils.py:765] 2026-10-21 19:11:34,611 >> loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +[INFO|configuration_utils.py:841] 2026-10-21 19:11:34,613 >> Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +[INFO|configuration_utils.py:765] 2026-10-21 19:11:34,751 >> loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +[INFO|configuration_utils.py:841] 2026-10-21 19:11:34,752 >> Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +[INFO|2026-10-21 19:11:35] llamafactory.data.loader:144 >> Loading dataset aya_slovak_300k_fixed.jsonl... +training example: +input_ids: +[20548, 336, 349, 396, 13126, 369, 13966, 264, 3638, 28723, 12018, 264, 2899, 369, 6582, 1999, 2691, 274, 272, 2159, 28723, 13, 13, 27332, 3133, 3112, 28747, 13, 28762, 3673, 554, 493, 28747, 945, 29365, 18765, 298, 287, 4362, 462, 28729, 5011, 710, 15200, 28820, 9041, 28725, 1683, 28709, 28725, 446, 6909, 28797, 686, 494, 328, 282, 28725, 486, 6125, 28709, 2513, 28728, 5240, 28710, 29114, 724, 4071, 300, 21775, 29114, 264, 14628, 28879, 332, 311, 6309, 29114, 11175, 4566, 3907, 710, 15200, 28820, 1189, 28718, 28723, 4702, 29365, 18765, 18336, 1683, 28709, 6670, 28862, 28827, 2340, 28709, 24949, 15690, 284, 8341, 2129, 300, 534, 760, 7264, 28725, 23103, 287, 4362, 724, 15504, 28725, 446, 6909, 28820, 295, 626, 271, 5945, 28723, 13, 13, 27332, 12107, 28747, 13, 2238, 2, 13, 13] +inputs: +Below is an instruction that describes a task. Write a response that appropriately completes the request. + +### Instruction: +Odsudok: keďže to bola lekcia prezentácie, meno, ktoré zavolal, by malo potvrdiť prítomnosť a tiež urobiť krátku prezentáciu. Keďže jej meno začínalo prvým písmenom abecedy, Ana bola prvá, ktorá hovorila. + +### Response: +ana + + +label_ids: +[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 2238, 2, 13, 13] +labels: +ana + + +eval example: +input_ids: +[20548, 336, 349, 396, 13126, 369, 13966, 264, 3638, 28723, 12018, 264, 2899, 369, 6582, 1999, 2691, 274, 272, 2159, 28723, 13, 13, 27332, 3133, 3112, 28747, 13, 28759, 377, 28827, 28873, 424, 11175, 4566, 4845, 9335, 783, 13470, 12577, 28889, 4321, 25933, 1879, 261, 28863, 532, 20659, 2390, 28747, 259, 23547, 18441, 1500, 17323, 676, 28879, 301, 3907, 13, 13, 27332, 12107, 28747, 13, 28798, 293, 8269, 28725, 8582, 2218, 11175, 4566, 4845, 4321, 25933, 28747, 14706, 17323, 9884, 4265, 28723, 5400, 28820, 637, 8866, 264, 268, 28863, 676, 28879, 21657, 332, 28879, 281, 3026, 712, 4845, 28723, 14706, 5666, 7747, 328, 1879, 15612, 667, 405, 28768, 264, 4321, 14029, 7264, 268, 28863, 668, 17760, 28723, 2, 13, 13] +inputs: +Below is an instruction that describes a task. Write a response that appropriately completes the request. + +### Instruction: +Napíšte krátky rozprávkový odsek na túto tému: Osoba X má manželku + +### Response: +Jasné, tu je krátky odsek: Monte má ženu. Volá sa Maria a sú manželia už dva roky. Monte ju stretol na vysokej a odvtedy sú spolu. + + +label_ids: +[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 28798, 293, 8269, 28725, 8582, 2218, 11175, 4566, 4845, 4321, 25933, 28747, 14706, 17323, 9884, 4265, 28723, 5400, 28820, 637, 8866, 264, 268, 28863, 676, 28879, 21657, 332, 28879, 281, 3026, 712, 4845, 28723, 14706, 5666, 7747, 328, 1879, 15612, 667, 405, 28768, 264, 4321, 14029, 7264, 268, 28863, 668, 17760, 28723, 2, 13, 13] +labels: +Jasné, tu je krátky odsek: Monte má ženu. Volá sa Maria a sú manželia už dva roky. Monte ju stretol na vysokej a odvtedy sú spolu. + + +[INFO|configuration_utils.py:765] 2026-10-21 19:11:36,353 >> loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +[INFO|configuration_utils.py:841] 2026-10-21 19:11:36,356 >> Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +[INFO|2026-10-21 19:11:36] llamafactory.model.model_utils.kv_cache:144 >> KV cache is disabled during training. +/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/unsloth/__init__.py:165: UserWarning: WARNING: Unsloth should be imported before [trl, transformers, peft] to ensure all optimizations are applied. Your code may run slower or encounter memory issues without these optimizations. + +Please restructure your imports with 'import unsloth' at the top of your file. + from ._gpu_init import * +🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning. +🦥 Unsloth Zoo will now patch everything to make training faster! +loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +Unsloth: WARNING `trust_remote_code` is True. +Are you certain you want to do remote code execution? +==((====))== Unsloth 2026.6.1: Fast Mistral patching. Transformers: 5.5.0. + \\ /| NVIDIA RTX A4500. Num GPUs = 1. Max memory: 19.573 GB. Platform: Linux. +O^O/ \_/ \ Torch: 2.6.0+cu124. CUDA: 8.6. CUDA Toolkit: 12.4. Triton: 3.2.0 +\ / Bfloat16 = TRUE. FA [Xformers = 0.0.29.post3. FA2 = False] + "-____-" Free license: http://github.com/unslothai/unsloth +Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored! +loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +loading weights file model.safetensors from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/model.safetensors.index.json +Will use dtype=torch.bfloat16 as defined in model's config object +Generate config GenerationConfig { + "bos_token_id": 1, + "eos_token_id": 2, + "output_attentions": false, + "output_hidden_states": false, + "pad_token_id": 2, + "use_cache": true +} + + Loading weights: 0%| | 0/291 [00:00). Using pad_token = . +/home/schwarc/LLaMA-Factory/src/llamafactory/model/model_utils/checkpointing.py:68: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead. + @torch.cuda.amp.custom_fwd +/home/schwarc/LLaMA-Factory/src/llamafactory/model/model_utils/checkpointing.py:85: FutureWarning: `torch.cuda.amp.custom_bwd(args...)` is deprecated. Please use `torch.amp.custom_bwd(args..., device_type='cuda')` instead. + @torch.cuda.amp.custom_bwd +[INFO|2026-10-21 19:11:53] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled. +[INFO|2026-10-21 19:11:53] llamafactory.model.adapter:144 >> Upcasting trainable params to float32. +[INFO|2026-10-21 19:11:53] llamafactory.model.adapter:144 >> Fine-tuning method: LoRA +Unsloth 2026.6.1 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers. +[INFO|2026-10-21 19:11:56] llamafactory.model.loader:144 >> trainable params: 41,943,040 || all params: 7,283,675,136 || trainable%: 0.5758 +The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'pad_token_id': 2}. +==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1 + \\ /| Num examples = 299,000 | Num Epochs = 1 | Total steps = 18,688 +O^O/ \_/ \ Batch size per device = 1 | Gradient accumulation steps = 16 +\ / Data Parallel GPUs = 1 | Total batch size (1 x 16 x 1) = 16 + "-____-" Trainable parameters = 41,943,040 of 7,283,675,136 (0.58% trained) + 0%| | 0/18688 [00:00> No metric eval_accuracy to plot. + +***** Running Evaluation ***** + Num examples = 1000 + Batch size = 1 +/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:71: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`. + warnings.warn(DEPRECATION_MESSAGE, FutureWarning) +/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:281: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`. + warnings.warn(DEPRECATION_MESSAGE, FutureWarning) + 0%| | 0/1000 [00:00> Process rank: 0, world size: 1, device: cuda:0, distributed training: False, compute dtype: torch.bfloat16 +Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads. +[INFO|configuration_utils.py:765] 2026-10-20 21:07:09,551 >> loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +[INFO|configuration_utils.py:841] 2026-10-20 21:07:09,559 >> Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +[INFO|configuration_utils.py:765] 2026-10-20 21:07:11,261 >> loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +[INFO|configuration_utils.py:841] 2026-10-20 21:07:11,265 >> Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +[INFO|configuration_utils.py:765] 2026-10-20 21:07:11,405 >> loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +[INFO|configuration_utils.py:841] 2026-10-20 21:07:11,407 >> Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +[INFO|2026-10-20 21:07:12] llamafactory.data.loader:144 >> Loading dataset aya_slovak_300k_fixed.jsonl... +training example: +input_ids: +[20548, 336, 349, 396, 13126, 369, 13966, 264, 3638, 28723, 12018, 264, 2899, 369, 6582, 1999, 2691, 274, 272, 2159, 28723, 13, 13, 27332, 3133, 3112, 28747, 13, 28762, 3673, 554, 493, 28747, 945, 29365, 18765, 298, 287, 4362, 462, 28729, 5011, 710, 15200, 28820, 9041, 28725, 1683, 28709, 28725, 446, 6909, 28797, 686, 494, 328, 282, 28725, 486, 6125, 28709, 2513, 28728, 5240, 28710, 29114, 724, 4071, 300, 21775, 29114, 264, 14628, 28879, 332, 311, 6309, 29114, 11175, 4566, 3907, 710, 15200, 28820, 1189, 28718, 28723, 4702, 29365, 18765, 18336, 1683, 28709, 6670, 28862, 28827, 2340, 28709, 24949, 15690, 284, 8341, 2129, 300, 534, 760, 7264, 28725, 23103, 287, 4362, 724, 15504, 28725, 446, 6909, 28820, 295, 626, 271, 5945, 28723, 13, 13, 27332, 12107, 28747, 13, 2238, 2, 13, 13] +inputs: +Below is an instruction that describes a task. Write a response that appropriately completes the request. + +### Instruction: +Odsudok: keďže to bola lekcia prezentácie, meno, ktoré zavolal, by malo potvrdiť prítomnosť a tiež urobiť krátku prezentáciu. Keďže jej meno začínalo prvým písmenom abecedy, Ana bola prvá, ktorá hovorila. + +### Response: +ana + + +label_ids: +[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 2238, 2, 13, 13] +labels: +ana + + +eval example: +input_ids: +[20548, 336, 349, 396, 13126, 369, 13966, 264, 3638, 28723, 12018, 264, 2899, 369, 6582, 1999, 2691, 274, 272, 2159, 28723, 13, 13, 27332, 3133, 3112, 28747, 13, 28759, 377, 28827, 28873, 424, 11175, 4566, 4845, 9335, 783, 13470, 12577, 28889, 4321, 25933, 1879, 261, 28863, 532, 20659, 2390, 28747, 259, 23547, 18441, 1500, 17323, 676, 28879, 301, 3907, 13, 13, 27332, 12107, 28747, 13, 28798, 293, 8269, 28725, 8582, 2218, 11175, 4566, 4845, 4321, 25933, 28747, 14706, 17323, 9884, 4265, 28723, 5400, 28820, 637, 8866, 264, 268, 28863, 676, 28879, 21657, 332, 28879, 281, 3026, 712, 4845, 28723, 14706, 5666, 7747, 328, 1879, 15612, 667, 405, 28768, 264, 4321, 14029, 7264, 268, 28863, 668, 17760, 28723, 2, 13, 13] +inputs: +Below is an instruction that describes a task. Write a response that appropriately completes the request. + +### Instruction: +Napíšte krátky rozprávkový odsek na túto tému: Osoba X má manželku + +### Response: +Jasné, tu je krátky odsek: Monte má ženu. Volá sa Maria a sú manželia už dva roky. Monte ju stretol na vysokej a odvtedy sú spolu. + + +label_ids: +[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 28798, 293, 8269, 28725, 8582, 2218, 11175, 4566, 4845, 4321, 25933, 28747, 14706, 17323, 9884, 4265, 28723, 5400, 28820, 637, 8866, 264, 268, 28863, 676, 28879, 21657, 332, 28879, 281, 3026, 712, 4845, 28723, 14706, 5666, 7747, 328, 1879, 15612, 667, 405, 28768, 264, 4321, 14029, 7264, 268, 28863, 668, 17760, 28723, 2, 13, 13] +labels: +Jasné, tu je krátky odsek: Monte má ženu. Volá sa Maria a sú manželia už dva roky. Monte ju stretol na vysokej a odvtedy sú spolu. + + +[INFO|configuration_utils.py:765] 2026-10-20 21:07:13,035 >> loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +[INFO|configuration_utils.py:841] 2026-10-20 21:07:13,038 >> Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +[INFO|2026-10-20 21:07:13] llamafactory.model.model_utils.kv_cache:144 >> KV cache is disabled during training. +/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/unsloth/__init__.py:165: UserWarning: WARNING: Unsloth should be imported before [trl, transformers, peft] to ensure all optimizations are applied. Your code may run slower or encounter memory issues without these optimizations. + +Please restructure your imports with 'import unsloth' at the top of your file. + from ._gpu_init import * +🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning. +🦥 Unsloth Zoo will now patch everything to make training faster! +loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +Unsloth: WARNING `trust_remote_code` is True. +Are you certain you want to do remote code execution? +==((====))== Unsloth 2026.6.1: Fast Mistral patching. Transformers: 5.5.0. + \\ /| NVIDIA RTX A4500. Num GPUs = 1. Max memory: 19.573 GB. Platform: Linux. +O^O/ \_/ \ Torch: 2.6.0+cu124. CUDA: 8.6. CUDA Toolkit: 12.4. Triton: 3.2.0 +\ / Bfloat16 = TRUE. FA [Xformers = 0.0.29.post3. FA2 = False] + "-____-" Free license: http://github.com/unslothai/unsloth +Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored! +loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +loading weights file model.safetensors from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/model.safetensors.index.json +Will use dtype=torch.bfloat16 as defined in model's config object +Generate config GenerationConfig { + "bos_token_id": 1, + "eos_token_id": 2, + "output_attentions": false, + "output_hidden_states": false, + "pad_token_id": 2, + "use_cache": true +} + + Loading weights: 0%| | 0/291 [00:00). Using pad_token = . +/home/schwarc/LLaMA-Factory/src/llamafactory/model/model_utils/checkpointing.py:68: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead. + @torch.cuda.amp.custom_fwd +/home/schwarc/LLaMA-Factory/src/llamafactory/model/model_utils/checkpointing.py:85: FutureWarning: `torch.cuda.amp.custom_bwd(args...)` is deprecated. Please use `torch.amp.custom_bwd(args..., device_type='cuda')` instead. + @torch.cuda.amp.custom_bwd +[INFO|2026-10-20 21:07:29] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled. +[INFO|2026-10-20 21:07:29] llamafactory.model.adapter:144 >> Upcasting trainable params to float32. +[INFO|2026-10-20 21:07:29] llamafactory.model.adapter:144 >> Fine-tuning method: LoRA +Unsloth 2026.6.1 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers. +[INFO|2026-10-20 21:07:33] llamafactory.model.loader:144 >> trainable params: 41,943,040 || all params: 7,283,675,136 || trainable%: 0.5758 +The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'pad_token_id': 2}. +==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1 + \\ /| Num examples = 299,000 | Num Epochs = 1 | Total steps = 18,688 +O^O/ \_/ \ Batch size per device = 1 | Gradient accumulation steps = 16 +\ / Data Parallel GPUs = 1 | Total batch size (1 x 16 x 1) = 16 + "-____-" Trainable parameters = 41,943,040 of 7,283,675,136 (0.58% trained) + 0%| | 0/18688 [00:00> No metric eval_accuracy to plot. + +***** Running Evaluation ***** + Num examples = 1000 + Batch size = 1 +/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:71: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`. + warnings.warn(DEPRECATION_MESSAGE, FutureWarning) +/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:281: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`. + warnings.warn(DEPRECATION_MESSAGE, FutureWarning) + 0%| | 0/1000 [00:00> Process rank: 0, world size: 1, device: cuda:0, distributed training: False, compute dtype: torch.bfloat16 +Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads. +[INFO|configuration_utils.py:765] 2026-10-19 22:49:42,966 >> loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +[INFO|configuration_utils.py:841] 2026-10-19 22:49:42,975 >> Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +[INFO|configuration_utils.py:765] 2026-10-19 22:49:44,585 >> loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +[INFO|configuration_utils.py:841] 2026-10-19 22:49:44,588 >> Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +[INFO|configuration_utils.py:765] 2026-10-19 22:49:44,728 >> loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +[INFO|configuration_utils.py:841] 2026-10-19 22:49:44,732 >> Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +[INFO|2026-10-19 22:49:45] llamafactory.data.loader:144 >> Loading dataset aya_slovak_300k_fixed.jsonl... +training example: +input_ids: +[20548, 336, 349, 396, 13126, 369, 13966, 264, 3638, 28723, 12018, 264, 2899, 369, 6582, 1999, 2691, 274, 272, 2159, 28723, 13, 13, 27332, 3133, 3112, 28747, 13, 28762, 3673, 554, 493, 28747, 945, 29365, 18765, 298, 287, 4362, 462, 28729, 5011, 710, 15200, 28820, 9041, 28725, 1683, 28709, 28725, 446, 6909, 28797, 686, 494, 328, 282, 28725, 486, 6125, 28709, 2513, 28728, 5240, 28710, 29114, 724, 4071, 300, 21775, 29114, 264, 14628, 28879, 332, 311, 6309, 29114, 11175, 4566, 3907, 710, 15200, 28820, 1189, 28718, 28723, 4702, 29365, 18765, 18336, 1683, 28709, 6670, 28862, 28827, 2340, 28709, 24949, 15690, 284, 8341, 2129, 300, 534, 760, 7264, 28725, 23103, 287, 4362, 724, 15504, 28725, 446, 6909, 28820, 295, 626, 271, 5945, 28723, 13, 13, 27332, 12107, 28747, 13, 2238, 2, 13, 13] +inputs: +Below is an instruction that describes a task. Write a response that appropriately completes the request. + +### Instruction: +Odsudok: keďže to bola lekcia prezentácie, meno, ktoré zavolal, by malo potvrdiť prítomnosť a tiež urobiť krátku prezentáciu. Keďže jej meno začínalo prvým písmenom abecedy, Ana bola prvá, ktorá hovorila. + +### Response: +ana + + +label_ids: +[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 2238, 2, 13, 13] +labels: +ana + + +eval example: +input_ids: +[20548, 336, 349, 396, 13126, 369, 13966, 264, 3638, 28723, 12018, 264, 2899, 369, 6582, 1999, 2691, 274, 272, 2159, 28723, 13, 13, 27332, 3133, 3112, 28747, 13, 28759, 377, 28827, 28873, 424, 11175, 4566, 4845, 9335, 783, 13470, 12577, 28889, 4321, 25933, 1879, 261, 28863, 532, 20659, 2390, 28747, 259, 23547, 18441, 1500, 17323, 676, 28879, 301, 3907, 13, 13, 27332, 12107, 28747, 13, 28798, 293, 8269, 28725, 8582, 2218, 11175, 4566, 4845, 4321, 25933, 28747, 14706, 17323, 9884, 4265, 28723, 5400, 28820, 637, 8866, 264, 268, 28863, 676, 28879, 21657, 332, 28879, 281, 3026, 712, 4845, 28723, 14706, 5666, 7747, 328, 1879, 15612, 667, 405, 28768, 264, 4321, 14029, 7264, 268, 28863, 668, 17760, 28723, 2, 13, 13] +inputs: +Below is an instruction that describes a task. Write a response that appropriately completes the request. + +### Instruction: +Napíšte krátky rozprávkový odsek na túto tému: Osoba X má manželku + +### Response: +Jasné, tu je krátky odsek: Monte má ženu. Volá sa Maria a sú manželia už dva roky. Monte ju stretol na vysokej a odvtedy sú spolu. + + +label_ids: +[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 28798, 293, 8269, 28725, 8582, 2218, 11175, 4566, 4845, 4321, 25933, 28747, 14706, 17323, 9884, 4265, 28723, 5400, 28820, 637, 8866, 264, 268, 28863, 676, 28879, 21657, 332, 28879, 281, 3026, 712, 4845, 28723, 14706, 5666, 7747, 328, 1879, 15612, 667, 405, 28768, 264, 4321, 14029, 7264, 268, 28863, 668, 17760, 28723, 2, 13, 13] +labels: +Jasné, tu je krátky odsek: Monte má ženu. Volá sa Maria a sú manželia už dva roky. Monte ju stretol na vysokej a odvtedy sú spolu. + + +[INFO|configuration_utils.py:765] 2026-10-19 22:49:46,265 >> loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +[INFO|configuration_utils.py:841] 2026-10-19 22:49:46,268 >> Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +[INFO|2026-10-19 22:49:46] llamafactory.model.model_utils.kv_cache:144 >> KV cache is disabled during training. +/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/unsloth/__init__.py:165: UserWarning: WARNING: Unsloth should be imported before [trl, transformers, peft] to ensure all optimizations are applied. Your code may run slower or encounter memory issues without these optimizations. + +Please restructure your imports with 'import unsloth' at the top of your file. + from ._gpu_init import * +🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning. +🦥 Unsloth Zoo will now patch everything to make training faster! +loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +Unsloth: WARNING `trust_remote_code` is True. +Are you certain you want to do remote code execution? +==((====))== Unsloth 2026.6.1: Fast Mistral patching. Transformers: 5.5.0. + \\ /| NVIDIA RTX A4500. Num GPUs = 1. Max memory: 19.573 GB. Platform: Linux. +O^O/ \_/ \ Torch: 2.6.0+cu124. CUDA: 8.6. CUDA Toolkit: 12.4. Triton: 3.2.0 +\ / Bfloat16 = TRUE. FA [Xformers = 0.0.29.post3. FA2 = False] + "-____-" Free license: http://github.com/unslothai/unsloth +Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored! +loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +loading weights file model.safetensors from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/model.safetensors.index.json +Will use dtype=torch.bfloat16 as defined in model's config object +Generate config GenerationConfig { + "bos_token_id": 1, + "eos_token_id": 2, + "output_attentions": false, + "output_hidden_states": false, + "pad_token_id": 2, + "use_cache": true +} + + Loading weights: 0%| | 0/291 [00:00). Using pad_token = . +/home/schwarc/LLaMA-Factory/src/llamafactory/model/model_utils/checkpointing.py:68: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead. + @torch.cuda.amp.custom_fwd +/home/schwarc/LLaMA-Factory/src/llamafactory/model/model_utils/checkpointing.py:85: FutureWarning: `torch.cuda.amp.custom_bwd(args...)` is deprecated. Please use `torch.amp.custom_bwd(args..., device_type='cuda')` instead. + @torch.cuda.amp.custom_bwd +[INFO|2026-10-19 22:50:03] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled. +[INFO|2026-10-19 22:50:03] llamafactory.model.adapter:144 >> Upcasting trainable params to float32. +[INFO|2026-10-19 22:50:03] llamafactory.model.adapter:144 >> Fine-tuning method: LoRA +Unsloth 2026.6.1 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers. +[INFO|2026-10-19 22:50:06] llamafactory.model.loader:144 >> trainable params: 41,943,040 || all params: 7,283,675,136 || trainable%: 0.5758 +The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'pad_token_id': 2}. +==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1 + \\ /| Num examples = 299,000 | Num Epochs = 1 | Total steps = 18,688 +O^O/ \_/ \ Batch size per device = 1 | Gradient accumulation steps = 16 +\ / Data Parallel GPUs = 1 | Total batch size (1 x 16 x 1) = 16 + "-____-" Trainable parameters = 41,943,040 of 7,283,675,136 (0.58% trained) + 0%| | 0/18688 [00:00> No metric eval_accuracy to plot. + +***** Running Evaluation ***** + Num examples = 1000 + Batch size = 1 +/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:71: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`. + warnings.warn(DEPRECATION_MESSAGE, FutureWarning) +/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:281: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`. + warnings.warn(DEPRECATION_MESSAGE, FutureWarning) + 0%| | 0/1000 [00:00> Process rank: 0, world size: 1, device: cuda:0, distributed training: False, compute dtype: torch.bfloat16 +Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads. +[INFO|configuration_utils.py:765] 2026-10-16 21:37:28,006 >> loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +[INFO|configuration_utils.py:841] 2026-10-16 21:37:28,016 >> Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +[INFO|configuration_utils.py:765] 2026-10-16 21:37:29,746 >> loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +[INFO|configuration_utils.py:841] 2026-10-16 21:37:29,750 >> Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +[INFO|configuration_utils.py:765] 2026-10-16 21:37:29,892 >> loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +[INFO|configuration_utils.py:841] 2026-10-16 21:37:29,896 >> Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +[INFO|2026-10-16 21:37:30] llamafactory.data.loader:144 >> Loading dataset aya_slovak_300k_fixed.jsonl... + Generating train split: 0 examples [00:00, ? examples/s] Generating train split: 67014 examples [00:00, 527486.70 examples/s] Generating train split: 178840 examples [00:00, 784565.77 examples/s] Generating train split: 300000 examples [00:00, 902405.86 examples/s] Generating train split: 300000 examples [00:00, 835527.05 examples/s] + Converting format of dataset: 0%| | 0/300000 [00:00 + + +label_ids: +[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 2238, 2, 13, 13] +labels: +ana + + + Running tokenizer on dataset: 0%| | 0/1000 [00:00 + + +label_ids: +[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 28798, 293, 8269, 28725, 8582, 2218, 11175, 4566, 4845, 4321, 25933, 28747, 14706, 17323, 9884, 4265, 28723, 5400, 28820, 637, 8866, 264, 268, 28863, 676, 28879, 21657, 332, 28879, 281, 3026, 712, 4845, 28723, 14706, 5666, 7747, 328, 1879, 15612, 667, 405, 28768, 264, 4321, 14029, 7264, 268, 28863, 668, 17760, 28723, 2, 13, 13] +labels: +Jasné, tu je krátky odsek: Monte má ženu. Volá sa Maria a sú manželia už dva roky. Monte ju stretol na vysokej a odvtedy sú spolu. + + +[INFO|configuration_utils.py:765] 2026-10-16 21:39:31,231 >> loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +[INFO|configuration_utils.py:841] 2026-10-16 21:39:31,234 >> Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +[INFO|2026-10-16 21:39:31] llamafactory.model.model_utils.kv_cache:144 >> KV cache is disabled during training. +/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/unsloth/__init__.py:165: UserWarning: WARNING: Unsloth should be imported before [trl, transformers, peft] to ensure all optimizations are applied. Your code may run slower or encounter memory issues without these optimizations. + +Please restructure your imports with 'import unsloth' at the top of your file. + from ._gpu_init import * +🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning. +🦥 Unsloth Zoo will now patch everything to make training faster! +loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +Unsloth: WARNING `trust_remote_code` is True. +Are you certain you want to do remote code execution? +==((====))== Unsloth 2026.6.1: Fast Mistral patching. Transformers: 5.5.0. + \\ /| NVIDIA RTX A4500. Num GPUs = 1. Max memory: 19.573 GB. Platform: Linux. +O^O/ \_/ \ Torch: 2.6.0+cu124. CUDA: 8.6. CUDA Toolkit: 12.4. Triton: 3.2.0 +\ / Bfloat16 = TRUE. FA [Xformers = 0.0.29.post3. FA2 = False] + "-____-" Free license: http://github.com/unslothai/unsloth +Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored! +loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +loading weights file model.safetensors from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/model.safetensors.index.json +Will use dtype=torch.bfloat16 as defined in model's config object +Generate config GenerationConfig { + "bos_token_id": 1, + "eos_token_id": 2, + "output_attentions": false, + "output_hidden_states": false, + "pad_token_id": 2, + "use_cache": true +} + + Loading weights: 0%| | 0/291 [00:00). Using pad_token = . +/home/schwarc/LLaMA-Factory/src/llamafactory/model/model_utils/checkpointing.py:68: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead. + @torch.cuda.amp.custom_fwd +/home/schwarc/LLaMA-Factory/src/llamafactory/model/model_utils/checkpointing.py:85: FutureWarning: `torch.cuda.amp.custom_bwd(args...)` is deprecated. Please use `torch.amp.custom_bwd(args..., device_type='cuda')` instead. + @torch.cuda.amp.custom_bwd +[INFO|2026-10-16 21:39:48] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled. +[INFO|2026-10-16 21:39:48] llamafactory.model.adapter:144 >> Upcasting trainable params to float32. +[INFO|2026-10-16 21:39:48] llamafactory.model.adapter:144 >> Fine-tuning method: LoRA +Unsloth 2026.6.1 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers. +[INFO|2026-10-16 21:39:51] llamafactory.model.loader:144 >> trainable params: 41,943,040 || all params: 7,283,675,136 || trainable%: 0.5758 +The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'pad_token_id': 2}. +==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1 + \\ /| Num examples = 299,000 | Num Epochs = 1 | Total steps = 18,688 +O^O/ \_/ \ Batch size per device = 1 | Gradient accumulation steps = 16 +\ / Data Parallel GPUs = 1 | Total batch size (1 x 16 x 1) = 16 + "-____-" Trainable parameters = 41,943,040 of 7,283,675,136 (0.58% trained) + 0%| | 0/18688 [00:00> No metric eval_accuracy to plot. + +***** Running Evaluation ***** + Num examples = 1000 + Batch size = 1 +/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:71: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`. + warnings.warn(DEPRECATION_MESSAGE, FutureWarning) +/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:281: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`. + warnings.warn(DEPRECATION_MESSAGE, FutureWarning) + 0%| | 0/1000 [00:00> Process rank: 0, world size: 1, device: cuda:0, distributed training: False, compute dtype: torch.bfloat16 +Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads. +[INFO|configuration_utils.py:765] 2026-10-17 19:52:56,675 >> loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +[INFO|configuration_utils.py:841] 2026-10-17 19:52:56,685 >> Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +[INFO|configuration_utils.py:765] 2026-10-17 19:52:58,322 >> loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +[INFO|configuration_utils.py:841] 2026-10-17 19:52:58,325 >> Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +[INFO|configuration_utils.py:765] 2026-10-17 19:52:58,466 >> loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +[INFO|configuration_utils.py:841] 2026-10-17 19:52:58,469 >> Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +[INFO|2026-10-17 19:52:59] llamafactory.data.loader:144 >> Loading dataset aya_slovak_300k_fixed.jsonl... +training example: +input_ids: +[20548, 336, 349, 396, 13126, 369, 13966, 264, 3638, 28723, 12018, 264, 2899, 369, 6582, 1999, 2691, 274, 272, 2159, 28723, 13, 13, 27332, 3133, 3112, 28747, 13, 28762, 3673, 554, 493, 28747, 945, 29365, 18765, 298, 287, 4362, 462, 28729, 5011, 710, 15200, 28820, 9041, 28725, 1683, 28709, 28725, 446, 6909, 28797, 686, 494, 328, 282, 28725, 486, 6125, 28709, 2513, 28728, 5240, 28710, 29114, 724, 4071, 300, 21775, 29114, 264, 14628, 28879, 332, 311, 6309, 29114, 11175, 4566, 3907, 710, 15200, 28820, 1189, 28718, 28723, 4702, 29365, 18765, 18336, 1683, 28709, 6670, 28862, 28827, 2340, 28709, 24949, 15690, 284, 8341, 2129, 300, 534, 760, 7264, 28725, 23103, 287, 4362, 724, 15504, 28725, 446, 6909, 28820, 295, 626, 271, 5945, 28723, 13, 13, 27332, 12107, 28747, 13, 2238, 2, 13, 13] +inputs: +Below is an instruction that describes a task. Write a response that appropriately completes the request. + +### Instruction: +Odsudok: keďže to bola lekcia prezentácie, meno, ktoré zavolal, by malo potvrdiť prítomnosť a tiež urobiť krátku prezentáciu. Keďže jej meno začínalo prvým písmenom abecedy, Ana bola prvá, ktorá hovorila. + +### Response: +ana + + +label_ids: +[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 2238, 2, 13, 13] +labels: +ana + + +eval example: +input_ids: +[20548, 336, 349, 396, 13126, 369, 13966, 264, 3638, 28723, 12018, 264, 2899, 369, 6582, 1999, 2691, 274, 272, 2159, 28723, 13, 13, 27332, 3133, 3112, 28747, 13, 28759, 377, 28827, 28873, 424, 11175, 4566, 4845, 9335, 783, 13470, 12577, 28889, 4321, 25933, 1879, 261, 28863, 532, 20659, 2390, 28747, 259, 23547, 18441, 1500, 17323, 676, 28879, 301, 3907, 13, 13, 27332, 12107, 28747, 13, 28798, 293, 8269, 28725, 8582, 2218, 11175, 4566, 4845, 4321, 25933, 28747, 14706, 17323, 9884, 4265, 28723, 5400, 28820, 637, 8866, 264, 268, 28863, 676, 28879, 21657, 332, 28879, 281, 3026, 712, 4845, 28723, 14706, 5666, 7747, 328, 1879, 15612, 667, 405, 28768, 264, 4321, 14029, 7264, 268, 28863, 668, 17760, 28723, 2, 13, 13] +inputs: +Below is an instruction that describes a task. Write a response that appropriately completes the request. + +### Instruction: +Napíšte krátky rozprávkový odsek na túto tému: Osoba X má manželku + +### Response: +Jasné, tu je krátky odsek: Monte má ženu. Volá sa Maria a sú manželia už dva roky. Monte ju stretol na vysokej a odvtedy sú spolu. + + +label_ids: +[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 28798, 293, 8269, 28725, 8582, 2218, 11175, 4566, 4845, 4321, 25933, 28747, 14706, 17323, 9884, 4265, 28723, 5400, 28820, 637, 8866, 264, 268, 28863, 676, 28879, 21657, 332, 28879, 281, 3026, 712, 4845, 28723, 14706, 5666, 7747, 328, 1879, 15612, 667, 405, 28768, 264, 4321, 14029, 7264, 268, 28863, 668, 17760, 28723, 2, 13, 13] +labels: +Jasné, tu je krátky odsek: Monte má ženu. Volá sa Maria a sú manželia už dva roky. Monte ju stretol na vysokej a odvtedy sú spolu. + + +[INFO|configuration_utils.py:765] 2026-10-17 19:52:59,960 >> loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +[INFO|configuration_utils.py:841] 2026-10-17 19:52:59,964 >> Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +[INFO|2026-10-17 19:52:59] llamafactory.model.model_utils.kv_cache:144 >> KV cache is disabled during training. +/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/unsloth/__init__.py:165: UserWarning: WARNING: Unsloth should be imported before [trl, transformers, peft] to ensure all optimizations are applied. Your code may run slower or encounter memory issues without these optimizations. + +Please restructure your imports with 'import unsloth' at the top of your file. + from ._gpu_init import * +🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning. +🦥 Unsloth Zoo will now patch everything to make training faster! +loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +Unsloth: WARNING `trust_remote_code` is True. +Are you certain you want to do remote code execution? +==((====))== Unsloth 2026.6.1: Fast Mistral patching. Transformers: 5.5.0. + \\ /| NVIDIA RTX A4500. Num GPUs = 1. Max memory: 19.573 GB. Platform: Linux. +O^O/ \_/ \ Torch: 2.6.0+cu124. CUDA: 8.6. CUDA Toolkit: 12.4. Triton: 3.2.0 +\ / Bfloat16 = TRUE. FA [Xformers = 0.0.29.post3. FA2 = False] + "-____-" Free license: http://github.com/unslothai/unsloth +Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored! +loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +loading weights file model.safetensors from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/model.safetensors.index.json +Will use dtype=torch.bfloat16 as defined in model's config object +Generate config GenerationConfig { + "bos_token_id": 1, + "eos_token_id": 2, + "output_attentions": false, + "output_hidden_states": false, + "pad_token_id": 2, + "use_cache": true +} + + Loading weights: 0%| | 0/291 [00:00). Using pad_token = . +/home/schwarc/LLaMA-Factory/src/llamafactory/model/model_utils/checkpointing.py:68: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead. + @torch.cuda.amp.custom_fwd +/home/schwarc/LLaMA-Factory/src/llamafactory/model/model_utils/checkpointing.py:85: FutureWarning: `torch.cuda.amp.custom_bwd(args...)` is deprecated. Please use `torch.amp.custom_bwd(args..., device_type='cuda')` instead. + @torch.cuda.amp.custom_bwd +[INFO|2026-10-17 19:53:17] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled. +[INFO|2026-10-17 19:53:17] llamafactory.model.adapter:144 >> Upcasting trainable params to float32. +[INFO|2026-10-17 19:53:17] llamafactory.model.adapter:144 >> Fine-tuning method: LoRA +Unsloth 2026.6.1 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers. +[INFO|2026-10-17 19:53:20] llamafactory.model.loader:144 >> trainable params: 41,943,040 || all params: 7,283,675,136 || trainable%: 0.5758 +The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'pad_token_id': 2}. +==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1 + \\ /| Num examples = 299,000 | Num Epochs = 1 | Total steps = 18,688 +O^O/ \_/ \ Batch size per device = 1 | Gradient accumulation steps = 16 +\ / Data Parallel GPUs = 1 | Total batch size (1 x 16 x 1) = 16 + "-____-" Trainable parameters = 41,943,040 of 7,283,675,136 (0.58% trained) + 0%| | 0/18688 [00:00> No metric eval_accuracy to plot. + +***** Running Evaluation ***** + Num examples = 1000 + Batch size = 1 +/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:71: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`. + warnings.warn(DEPRECATION_MESSAGE, FutureWarning) +/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:281: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`. + warnings.warn(DEPRECATION_MESSAGE, FutureWarning) + 0%| | 0/1000 [00:00> Process rank: 0, world size: 1, device: cuda:0, distributed training: False, compute dtype: torch.bfloat16 +[INFO|configuration_utils.py:765] 2026-10-18 18:03:24,138 >> loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +[INFO|configuration_utils.py:841] 2026-10-18 18:03:24,147 >> Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads. +[INFO|configuration_utils.py:765] 2026-10-18 18:03:26,022 >> loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +[INFO|configuration_utils.py:841] 2026-10-18 18:03:26,025 >> Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +[INFO|configuration_utils.py:765] 2026-10-18 18:03:26,175 >> loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +[INFO|configuration_utils.py:841] 2026-10-18 18:03:26,178 >> Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +[INFO|2026-10-18 18:03:26] llamafactory.data.loader:144 >> Loading dataset aya_slovak_300k_fixed.jsonl... +training example: +input_ids: +[20548, 336, 349, 396, 13126, 369, 13966, 264, 3638, 28723, 12018, 264, 2899, 369, 6582, 1999, 2691, 274, 272, 2159, 28723, 13, 13, 27332, 3133, 3112, 28747, 13, 28762, 3673, 554, 493, 28747, 945, 29365, 18765, 298, 287, 4362, 462, 28729, 5011, 710, 15200, 28820, 9041, 28725, 1683, 28709, 28725, 446, 6909, 28797, 686, 494, 328, 282, 28725, 486, 6125, 28709, 2513, 28728, 5240, 28710, 29114, 724, 4071, 300, 21775, 29114, 264, 14628, 28879, 332, 311, 6309, 29114, 11175, 4566, 3907, 710, 15200, 28820, 1189, 28718, 28723, 4702, 29365, 18765, 18336, 1683, 28709, 6670, 28862, 28827, 2340, 28709, 24949, 15690, 284, 8341, 2129, 300, 534, 760, 7264, 28725, 23103, 287, 4362, 724, 15504, 28725, 446, 6909, 28820, 295, 626, 271, 5945, 28723, 13, 13, 27332, 12107, 28747, 13, 2238, 2, 13, 13] +inputs: +Below is an instruction that describes a task. Write a response that appropriately completes the request. + +### Instruction: +Odsudok: keďže to bola lekcia prezentácie, meno, ktoré zavolal, by malo potvrdiť prítomnosť a tiež urobiť krátku prezentáciu. Keďže jej meno začínalo prvým písmenom abecedy, Ana bola prvá, ktorá hovorila. + +### Response: +ana + + +label_ids: +[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 2238, 2, 13, 13] +labels: +ana + + +eval example: +input_ids: +[20548, 336, 349, 396, 13126, 369, 13966, 264, 3638, 28723, 12018, 264, 2899, 369, 6582, 1999, 2691, 274, 272, 2159, 28723, 13, 13, 27332, 3133, 3112, 28747, 13, 28759, 377, 28827, 28873, 424, 11175, 4566, 4845, 9335, 783, 13470, 12577, 28889, 4321, 25933, 1879, 261, 28863, 532, 20659, 2390, 28747, 259, 23547, 18441, 1500, 17323, 676, 28879, 301, 3907, 13, 13, 27332, 12107, 28747, 13, 28798, 293, 8269, 28725, 8582, 2218, 11175, 4566, 4845, 4321, 25933, 28747, 14706, 17323, 9884, 4265, 28723, 5400, 28820, 637, 8866, 264, 268, 28863, 676, 28879, 21657, 332, 28879, 281, 3026, 712, 4845, 28723, 14706, 5666, 7747, 328, 1879, 15612, 667, 405, 28768, 264, 4321, 14029, 7264, 268, 28863, 668, 17760, 28723, 2, 13, 13] +inputs: +Below is an instruction that describes a task. Write a response that appropriately completes the request. + +### Instruction: +Napíšte krátky rozprávkový odsek na túto tému: Osoba X má manželku + +### Response: +Jasné, tu je krátky odsek: Monte má ženu. Volá sa Maria a sú manželia už dva roky. Monte ju stretol na vysokej a odvtedy sú spolu. + + +label_ids: +[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 28798, 293, 8269, 28725, 8582, 2218, 11175, 4566, 4845, 4321, 25933, 28747, 14706, 17323, 9884, 4265, 28723, 5400, 28820, 637, 8866, 264, 268, 28863, 676, 28879, 21657, 332, 28879, 281, 3026, 712, 4845, 28723, 14706, 5666, 7747, 328, 1879, 15612, 667, 405, 28768, 264, 4321, 14029, 7264, 268, 28863, 668, 17760, 28723, 2, 13, 13] +labels: +Jasné, tu je krátky odsek: Monte má ženu. Volá sa Maria a sú manželia už dva roky. Monte ju stretol na vysokej a odvtedy sú spolu. + + +[INFO|configuration_utils.py:765] 2026-10-18 18:03:27,681 >> loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +[INFO|configuration_utils.py:841] 2026-10-18 18:03:27,684 >> Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +[INFO|2026-10-18 18:03:27] llamafactory.model.model_utils.kv_cache:144 >> KV cache is disabled during training. +/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/unsloth/__init__.py:165: UserWarning: WARNING: Unsloth should be imported before [trl, transformers, peft] to ensure all optimizations are applied. Your code may run slower or encounter memory issues without these optimizations. + +Please restructure your imports with 'import unsloth' at the top of your file. + from ._gpu_init import * +🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning. +🦥 Unsloth Zoo will now patch everything to make training faster! +loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +Unsloth: WARNING `trust_remote_code` is True. +Are you certain you want to do remote code execution? +==((====))== Unsloth 2026.6.1: Fast Mistral patching. Transformers: 5.5.0. + \\ /| NVIDIA RTX A4500. Num GPUs = 1. Max memory: 19.573 GB. Platform: Linux. +O^O/ \_/ \ Torch: 2.6.0+cu124. CUDA: 8.6. CUDA Toolkit: 12.4. Triton: 3.2.0 +\ / Bfloat16 = TRUE. FA [Xformers = 0.0.29.post3. FA2 = False] + "-____-" Free license: http://github.com/unslothai/unsloth +Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored! +loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json +Model config MistralConfig { + "architectures": [ + "MistralForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 1, + "dtype": "bfloat16", + "eos_token_id": 2, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 32768, + "model_type": "mistral", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pad_token_id": 2, + "rms_norm_eps": 1e-05, + "rope_parameters": { + "rope_theta": 10000.0, + "rope_type": "default" + }, + "sliding_window": 4096, + "tie_word_embeddings": false, + "transformers_version": "5.5.0", + "use_cache": true, + "vocab_size": 32000 +} + +loading weights file model.safetensors from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/model.safetensors.index.json +Will use dtype=torch.bfloat16 as defined in model's config object +Generate config GenerationConfig { + "bos_token_id": 1, + "eos_token_id": 2, + "output_attentions": false, + "output_hidden_states": false, + "pad_token_id": 2, + "use_cache": true +} + + Loading weights: 0%| | 0/291 [00:00). Using pad_token = . +/home/schwarc/LLaMA-Factory/src/llamafactory/model/model_utils/checkpointing.py:68: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead. + @torch.cuda.amp.custom_fwd +/home/schwarc/LLaMA-Factory/src/llamafactory/model/model_utils/checkpointing.py:85: FutureWarning: `torch.cuda.amp.custom_bwd(args...)` is deprecated. Please use `torch.amp.custom_bwd(args..., device_type='cuda')` instead. + @torch.cuda.amp.custom_bwd +[INFO|2026-10-18 18:03:47] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled. +[INFO|2026-10-18 18:03:47] llamafactory.model.adapter:144 >> Upcasting trainable params to float32. +[INFO|2026-10-18 18:03:47] llamafactory.model.adapter:144 >> Fine-tuning method: LoRA +Unsloth 2026.6.1 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers. +[INFO|2026-10-18 18:03:50] llamafactory.model.loader:144 >> trainable params: 41,943,040 || all params: 7,283,675,136 || trainable%: 0.5758 +The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'pad_token_id': 2}. +==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1 + \\ /| Num examples = 299,000 | Num Epochs = 1 | Total steps = 18,688 +O^O/ \_/ \ Batch size per device = 1 | Gradient accumulation steps = 16 +\ / Data Parallel GPUs = 1 | Total batch size (1 x 16 x 1) = 16 + "-____-" Trainable parameters = 41,943,040 of 7,283,675,136 (0.58% trained) + 0%| | 0/18688 [00:00> No metric eval_accuracy to plot. + +***** Running Evaluation ***** + Num examples = 1000 + Batch size = 1 +/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:71: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`. + warnings.warn(DEPRECATION_MESSAGE, FutureWarning) +/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:281: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. 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+8e-05,lr_0p00008,18688,1.0,18688,0.875216543674469,18688,0.875216543674469,9.149866104125977,1.5837211608886719,79243.6449,19,1868 +0.0001,lr_0p00010,18688,1.0,18688,0.87261962890625,18688,0.87261962890625,8.578277587890625,1.6475107669830322,79344.5851,19,1868 +0.00015,lr_0p00015,18688,1.0,18688,0.8842697143554688,18688,0.8842697143554688,28.051427841186523,1.9590225219726562,80127.6428,19,1868 +0.00018,lr_0p00018,18688,1.0,18688,0.8914602994918823,18000,0.891387939453125,6.5584211349487305,2.163313627243042,79892.0077,19,1868 +0.0002,lr_0p00020,18688,1.0,18688,0.8976690173149109,18000,0.897506594657898,10.969125747680664,2.306173801422119,79710.3252,19,1868 +0.00023,lr_0p00023,18688,1.0,18688,0.9032362699508667,18000,0.9031931161880493,23.10581398010254,2.501060724258423,79789.3562,19,1868 diff --git a/aya_300k_lr_experiments/results/training_run_summary.json b/aya_300k_lr_experiments/results/training_run_summary.json new file mode 100644 index 0000000..1189d38 --- /dev/null +++ b/aya_300k_lr_experiments/results/training_run_summary.json @@ -0,0 +1,92 @@ +[ + { + "learning_rate": 8e-05, + "run": "lr_0p00008", + "global_step": 18688, + "epoch": 1.0, + "final_eval_step": 18688, + "final_eval_loss": 0.875216543674469, + "min_eval_step": 18688, + "min_eval_loss": 0.875216543674469, + "max_grad_norm": 9.149866104125977, + "p95_grad_norm": 1.5837211608886719, + "train_runtime": 79243.6449, + "eval_observations": 19, + "gradient_observations": 1868 + }, + { + "learning_rate": 0.0001, + "run": "lr_0p00010", + "global_step": 18688, + "epoch": 1.0, + "final_eval_step": 18688, + "final_eval_loss": 0.87261962890625, + "min_eval_step": 18688, + "min_eval_loss": 0.87261962890625, + "max_grad_norm": 8.578277587890625, + "p95_grad_norm": 1.6475107669830322, + "train_runtime": 79344.5851, + "eval_observations": 19, + "gradient_observations": 1868 + }, + { + "learning_rate": 0.00015, + "run": "lr_0p00015", + "global_step": 18688, + "epoch": 1.0, + "final_eval_step": 18688, + "final_eval_loss": 0.8842697143554688, + "min_eval_step": 18688, + "min_eval_loss": 0.8842697143554688, + "max_grad_norm": 28.051427841186523, + "p95_grad_norm": 1.9590225219726562, + "train_runtime": 80127.6428, + "eval_observations": 19, + "gradient_observations": 1868 + }, + { + "learning_rate": 0.00018, + "run": "lr_0p00018", + "global_step": 18688, + "epoch": 1.0, + "final_eval_step": 18688, + "final_eval_loss": 0.8914602994918823, + "min_eval_step": 18000, + "min_eval_loss": 0.891387939453125, + "max_grad_norm": 6.5584211349487305, + "p95_grad_norm": 2.163313627243042, + "train_runtime": 79892.0077, + "eval_observations": 19, + "gradient_observations": 1868 + }, + { + "learning_rate": 0.0002, + "run": "lr_0p00020", + "global_step": 18688, + "epoch": 1.0, + "final_eval_step": 18688, + "final_eval_loss": 0.8976690173149109, + "min_eval_step": 18000, + "min_eval_loss": 0.897506594657898, + "max_grad_norm": 10.969125747680664, + "p95_grad_norm": 2.306173801422119, + "train_runtime": 79710.3252, + "eval_observations": 19, + "gradient_observations": 1868 + }, + { + "learning_rate": 0.00023, + "run": "lr_0p00023", + "global_step": 18688, + "epoch": 1.0, + "final_eval_step": 18688, + "final_eval_loss": 0.9032362699508667, + "min_eval_step": 18000, + "min_eval_loss": 0.9031931161880493, + "max_grad_norm": 23.10581398010254, + "p95_grad_norm": 2.501060724258423, + "train_runtime": 79789.3562, + "eval_observations": 19, + "gradient_observations": 1868 + } +] diff --git a/aya_300k_lr_experiments/scripts/analyze_aya_300k_lr.py b/aya_300k_lr_experiments/scripts/analyze_aya_300k_lr.py new file mode 100644 index 0000000..ad122ef --- /dev/null +++ b/aya_300k_lr_experiments/scripts/analyze_aya_300k_lr.py @@ -0,0 +1,338 @@ +from pathlib import Path +import csv +import json +import math + + +ROOT = Path( + "/home/schwarc/diplomovka/" + "aya_300k_lr_validation" +) + +RESULT_DIR = ROOT / "results" +RESULT_DIR.mkdir( + parents=True, + exist_ok=True +) + + +RUNS = [ + ("0.00018", ROOT / "lr_0p00018"), + ("0.00020", ROOT / "lr_0p00020"), + ("0.00023", ROOT / "lr_0p00023"), +] + + +def load_best_state(output): + + files = list( + output.rglob( + "trainer_state.json" + ) + ) + + if not files: + return None + + candidates = [] + + for p in files: + try: + data = json.loads( + p.read_text( + encoding="utf-8" + ) + ) + + candidates.append( + ( + int( + data.get( + "global_step", + 0 + ) + ), + data, + p, + ) + ) + + except Exception: + pass + + if not candidates: + return None + + return max( + candidates, + key=lambda x: x[0] + ) + + +rows = [] +eval_curves = {} +grad_curves = {} + + +for lr, output in RUNS: + + result = load_best_state( + output + ) + + if result is None: + continue + + global_step, state, state_file = result + + history = state.get( + "log_history", + [] + ) + + evals = [] + grads = [] + + for item in history: + + step = int( + item.get("step", 0) + ) + + if "eval_loss" in item: + try: + value = float( + item["eval_loss"] + ) + if math.isfinite(value): + evals.append( + (step, value) + ) + except Exception: + pass + + if "grad_norm" in item: + try: + value = float( + item["grad_norm"] + ) + if math.isfinite(value): + grads.append( + (step, value) + ) + except Exception: + pass + + + if evals: + final_eval_step, final_eval = ( + evals[-1] + ) + + min_eval_step, min_eval = min( + evals, + key=lambda x: x[1] + ) + else: + final_eval_step = "" + final_eval = "" + min_eval_step = "" + min_eval = "" + + + grad_values = [ + value + for _, value in grads + ] + + if grad_values: + + max_grad = max( + grad_values + ) + + ordered = sorted( + grad_values + ) + + p95_index = int( + 0.95 + * (len(ordered) - 1) + ) + + p95_grad = ( + ordered[p95_index] + ) + + else: + max_grad = "" + p95_grad = "" + + + rows.append( + { + "learning_rate": lr, + "global_step": global_step, + "final_eval_step": + final_eval_step, + "final_eval_loss": + final_eval, + "min_eval_step": + min_eval_step, + "min_eval_loss": + min_eval, + "max_grad_norm": + max_grad, + "p95_grad_norm": + p95_grad, + "trainer_state": + str(state_file), + } + ) + + eval_curves[lr] = evals + grad_curves[lr] = grads + + +csv_path = ( + RESULT_DIR + / "aya_300k_lr_summary.csv" +) + +with csv_path.open( + "w", + encoding="utf-8", + newline="" +) as f: + + fields = [ + "learning_rate", + "global_step", + "final_eval_step", + "final_eval_loss", + "min_eval_step", + "min_eval_loss", + "max_grad_norm", + "p95_grad_norm", + "trainer_state", + ] + + writer = csv.DictWriter( + f, + fieldnames=fields + ) + + writer.writeheader() + writer.writerows(rows) + + +print() +print("===== 300K LR SUMMARY =====") + +for row in rows: + print( + f"LR={row['learning_rate']} | " + f"step={row['global_step']} | " + f"final_eval={row['final_eval_loss']} | " + f"min_eval={row['min_eval_loss']} " + f"@ {row['min_eval_step']} | " + f"max_grad={row['max_grad_norm']} | " + f"p95_grad={row['p95_grad_norm']}" + ) + +print() +print("CSV:", csv_path) + + +try: + import matplotlib.pyplot as plt + + if eval_curves: + + plt.figure( + figsize=(12, 7) + ) + + for lr, points in ( + eval_curves.items() + ): + if not points: + continue + + plt.plot( + [x for x, _ in points], + [y for _, y in points], + marker="o", + label=f"LR {lr}", + ) + + plt.xlabel( + "optimizer step" + ) + plt.ylabel( + "eval loss" + ) + plt.title( + "Aya 300k - eval loss" + ) + plt.legend() + plt.grid(alpha=0.25) + plt.tight_layout() + + plt.savefig( + RESULT_DIR + / "eval_loss_over_steps.png", + dpi=200, + bbox_inches="tight" + ) + + plt.close() + + + if grad_curves: + + plt.figure( + figsize=(12, 7) + ) + + for lr, points in ( + grad_curves.items() + ): + if not points: + continue + + plt.plot( + [x for x, _ in points], + [y for _, y in points], + label=f"LR {lr}", + ) + + plt.xlabel( + "optimizer step" + ) + plt.ylabel( + "grad norm" + ) + plt.title( + "Aya 300k - gradient norm" + ) + plt.yscale("log") + plt.legend() + plt.grid(alpha=0.25) + plt.tight_layout() + + plt.savefig( + RESULT_DIR + / "grad_norm_over_steps.png", + dpi=200, + bbox_inches="tight" + ) + + plt.close() + + +except Exception as exc: + print( + "Grafy sa nepodarilo vytvorit:", + exc + ) diff --git a/aya_300k_lr_experiments/scripts/analyze_aya_300k_lr_all.py b/aya_300k_lr_experiments/scripts/analyze_aya_300k_lr_all.py new file mode 100644 index 0000000..c334300 --- /dev/null +++ b/aya_300k_lr_experiments/scripts/analyze_aya_300k_lr_all.py @@ -0,0 +1,341 @@ +from pathlib import Path +import csv +import json +import math + + +ROOT = Path( + "/home/schwarc/diplomovka/" + "aya_300k_lr_validation" +) + +RESULT_DIR = ROOT / "results" +RESULT_DIR.mkdir( + parents=True, + exist_ok=True +) + + +RUNS = [ + ("0.00008", ROOT / "lr_0p00008"), + ("0.00010", ROOT / "lr_0p00010"), + ("0.00015", ROOT / "lr_0p00015"), + ("0.00018", ROOT / "lr_0p00018"), + ("0.00020", ROOT / "lr_0p00020"), + ("0.00023", ROOT / "lr_0p00023"), +] + + +def load_best_state(output): + + files = list( + output.rglob( + "trainer_state.json" + ) + ) + + if not files: + return None + + candidates = [] + + for p in files: + try: + data = json.loads( + p.read_text( + encoding="utf-8" + ) + ) + + candidates.append( + ( + int( + data.get( + "global_step", + 0 + ) + ), + data, + p, + ) + ) + + except Exception: + pass + + if not candidates: + return None + + return max( + candidates, + key=lambda x: x[0] + ) + + +rows = [] +eval_curves = {} +grad_curves = {} + + +for lr, output in RUNS: + + result = load_best_state( + output + ) + + if result is None: + continue + + global_step, state, state_file = result + + history = state.get( + "log_history", + [] + ) + + evals = [] + grads = [] + + for item in history: + + step = int( + item.get("step", 0) + ) + + if "eval_loss" in item: + try: + value = float( + item["eval_loss"] + ) + if math.isfinite(value): + evals.append( + (step, value) + ) + except Exception: + pass + + if "grad_norm" in item: + try: + value = float( + item["grad_norm"] + ) + if math.isfinite(value): + grads.append( + (step, value) + ) + except Exception: + pass + + + if evals: + final_eval_step, final_eval = ( + evals[-1] + ) + + min_eval_step, min_eval = min( + evals, + key=lambda x: x[1] + ) + else: + final_eval_step = "" + final_eval = "" + min_eval_step = "" + min_eval = "" + + + grad_values = [ + value + for _, value in grads + ] + + if grad_values: + + max_grad = max( + grad_values + ) + + ordered = sorted( + grad_values + ) + + p95_index = int( + 0.95 + * (len(ordered) - 1) + ) + + p95_grad = ( + ordered[p95_index] + ) + + else: + max_grad = "" + p95_grad = "" + + + rows.append( + { + "learning_rate": lr, + "global_step": global_step, + "final_eval_step": + final_eval_step, + "final_eval_loss": + final_eval, + "min_eval_step": + min_eval_step, + "min_eval_loss": + min_eval, + "max_grad_norm": + max_grad, + "p95_grad_norm": + p95_grad, + "trainer_state": + str(state_file), + } + ) + + eval_curves[lr] = evals + grad_curves[lr] = grads + + +csv_path = ( + RESULT_DIR + / "aya_300k_lr_summary.csv" +) + +with csv_path.open( + "w", + encoding="utf-8", + newline="" +) as f: + + fields = [ + "learning_rate", + "global_step", + "final_eval_step", + "final_eval_loss", + "min_eval_step", + "min_eval_loss", + "max_grad_norm", + "p95_grad_norm", + "trainer_state", + ] + + writer = csv.DictWriter( + f, + fieldnames=fields + ) + + writer.writeheader() + writer.writerows(rows) + + +print() +print("===== 300K LR SUMMARY =====") + +for row in rows: + print( + f"LR={row['learning_rate']} | " + f"step={row['global_step']} | " + f"final_eval={row['final_eval_loss']} | " + f"min_eval={row['min_eval_loss']} " + f"@ {row['min_eval_step']} | " + f"max_grad={row['max_grad_norm']} | " + f"p95_grad={row['p95_grad_norm']}" + ) + +print() +print("CSV:", csv_path) + + +try: + import matplotlib.pyplot as plt + + if eval_curves: + + plt.figure( + figsize=(12, 7) + ) + + for lr, points in ( + eval_curves.items() + ): + if not points: + continue + + plt.plot( + [x for x, _ in points], + [y for _, y in points], + marker="o", + label=f"LR {lr}", + ) + + plt.xlabel( + "optimizer step" + ) + plt.ylabel( + "eval loss" + ) + plt.title( + "Aya 300k - eval loss" + ) + plt.legend() + plt.grid(alpha=0.25) + plt.tight_layout() + + plt.savefig( + RESULT_DIR + / "eval_loss_over_steps.png", + dpi=200, + bbox_inches="tight" + ) + + plt.close() + + + if grad_curves: + + plt.figure( + figsize=(12, 7) + ) + + for lr, points in ( + grad_curves.items() + ): + if not points: + continue + + plt.plot( + [x for x, _ in points], + [y for _, y in points], + label=f"LR {lr}", + ) + + plt.xlabel( + "optimizer step" + ) + plt.ylabel( + "grad norm" + ) + plt.title( + "Aya 300k - gradient norm" + ) + plt.yscale("log") + plt.legend() + plt.grid(alpha=0.25) + plt.tight_layout() + + plt.savefig( + RESULT_DIR + / "grad_norm_over_steps.png", + dpi=200, + bbox_inches="tight" + ) + + plt.close() + + +except Exception as exc: + print( + "Grafy sa nepodarilo vytvorit:", + exc + ) diff --git a/aya_300k_lr_experiments/scripts/analyze_training_results.py b/aya_300k_lr_experiments/scripts/analyze_training_results.py new file mode 100644 index 0000000..ea8ad64 --- /dev/null +++ b/aya_300k_lr_experiments/scripts/analyze_training_results.py @@ -0,0 +1,119 @@ +#!/usr/bin/env python3 +"""Recompute run summaries and the LR comparison from compact curated evidence. + +Run with no arguments from any directory. The standard library produces the CSV +and JSON summaries; matplotlib is required only for the PNG chart. No trainer +state, checkpoints, model weights, or tokenizers are needed. + +The gradient P95 matches the archived summary: sort N observed norms and choose +the zero-based index floor(0.95 * (N - 1)), without interpolation. +""" +import csv +import json +import math +from collections import defaultdict +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[1] +RESULTS = ROOT / "results" + + +def read_rows(filename): + with (RESULTS / filename).open(newline="", encoding="utf-8") as handle: + return list(csv.DictReader(handle)) + + +def summarize(): + evaluations = defaultdict(list) + gradients = defaultdict(list) + for row in read_rows("eval_loss_history.csv"): + evaluations[row["run"]].append(row) + for row in read_rows("gradient_history.csv"): + gradients[row["run"]].append(float(row["grad_norm"])) + raw_summary = {float(row["learning_rate"]): row for row in read_rows("aya_300k_lr_summary.csv")} + summaries = [] + for run, rows in sorted(evaluations.items()): + rows.sort(key=lambda row: int(row["step"])) + lr = float(rows[0]["learning_rate"]) + if any(float(row["learning_rate"]) != lr for row in rows): + raise ValueError(f"Inconsistent learning rate in {run}") + trial = ROOT / "trials" / run + train = json.loads((trial / "train_results.json").read_text(encoding="utf-8")) + final_eval = json.loads((trial / "eval_results.json").read_text(encoding="utf-8")) + norms = sorted(gradients[run]) + if not norms or not (trial / "TRAINING_COMPLETE").is_file(): + raise ValueError(f"Incomplete evidence for {run}") + final = rows[-1] + best = min(rows, key=lambda row: float(row["eval_loss"])) + summary = { + "learning_rate": lr, "run": run, + "global_step": int(raw_summary[lr]["global_step"]), + "epoch": float(train["epoch"]), + "final_eval_step": int(final["step"]), + "final_eval_loss": float(final["eval_loss"]), + "min_eval_step": int(best["step"]), + "min_eval_loss": float(best["eval_loss"]), + "max_grad_norm": max(norms), + "p95_grad_norm": norms[math.floor(0.95 * (len(norms) - 1))], + "train_runtime": float(train["train_runtime"]), + "eval_observations": len(rows), + "gradient_observations": len(norms), + } + if summary["final_eval_loss"] != float(final_eval["eval_loss"]): + raise ValueError(f"Final evaluation disagrees with eval_results.json: {run}") + for field in ("global_step", "final_eval_step", "final_eval_loss", "min_eval_step", + "min_eval_loss", "max_grad_norm", "p95_grad_norm"): + if float(summary[field]) != float(raw_summary[lr][field]): + raise ValueError(f"Archived summary disagrees at {run}: {field}") + summaries.append(summary) + if len(summaries) != 6 or any(row["global_step"] != 18688 or row["epoch"] != 1.0 + or row["eval_observations"] != 19 or row["gradient_observations"] != 1868 + for row in summaries): + raise ValueError("Expected six completed one-epoch runs with 19 eval and 1,868 gradient observations each") + with (RESULTS / "training_run_summary.csv").open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=list(summaries[0])) + writer.writeheader() + writer.writerows(summaries) + (RESULTS / "training_run_summary.json").write_text(json.dumps(summaries, indent=2) + "\n", encoding="utf-8") + return summaries + + +def plot(summaries): + try: + import matplotlib + matplotlib.use("Agg") + import matplotlib.pyplot as plt + except ImportError as exc: + raise SystemExit("Summaries recomputed. Install matplotlib to regenerate the PNG chart.") from exc + plt.rcParams.update({"font.family": "DejaVu Sans", "font.size": 11}) + fig, ax = plt.subplots(figsize=(8, 4.8), layout="constrained") + x = [row["learning_rate"] * 1e4 for row in summaries] + y = [row["final_eval_loss"] for row in summaries] + ax.plot(x, y, color="#335c81", marker="o", linewidth=1.6, markersize=6) + selected = min(summaries, key=lambda row: row["final_eval_loss"]) + sx, sy = selected["learning_rate"] * 1e4, selected["final_eval_loss"] + ax.scatter([sx], [sy], color="#ba4a00", s=75, zorder=3) + for xi, yi in zip(x, y): + ax.annotate(f"{yi:.5f}", (xi, yi), xytext=(0, 10), textcoords="offset points", ha="center", fontsize=9) + ax.annotate("Selected LR = 0.00010", (sx, sy), xytext=(20, -24), + textcoords="offset points", color="#8a3700", fontsize=10) + ax.set(title="Aya 300k: final validation loss by learning rate", + xlabel="Learning rate (×10⁻⁴)", ylabel="Final validation loss (lower is better)") + ax.set_xticks(x, [f"{value:.1f}" for value in x]) + ax.set_ylim(min(y) - 0.009, max(y) + 0.012) + ax.grid(axis="y", color="#d8dee5", linewidth=0.7) + ax.spines[["top", "right"]].set_visible(False) + fig.savefig(RESULTS / "lr_vs_final_eval_loss.png", dpi=200, metadata={"Description": "Six manually chosen learning rates; final evaluation at step 18688 after one epoch."}) + plt.close(fig) + + +def main(): + summaries = summarize() + plot(summaries) + best = min(summaries, key=lambda row: row["final_eval_loss"]) + print(f"Verified {len(summaries)} runs. Selected {best['run']}: final eval loss {best['final_eval_loss']:.15g}") + print(f"Saved summaries and chart in {RESULTS}") + + +if __name__ == "__main__": + main() diff --git a/aya_300k_lr_experiments/scripts/rerun_skbench_lr_0p00010.sh b/aya_300k_lr_experiments/scripts/rerun_skbench_lr_0p00010.sh new file mode 100644 index 0000000..a0dc63d --- /dev/null +++ b/aya_300k_lr_experiments/scripts/rerun_skbench_lr_0p00010.sh @@ -0,0 +1,37 @@ +#!/usr/bin/env bash +# Reconstructed from the completed run's saved parameters and frozen task list. +set -euo pipefail + +PROJECT=$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")/.." && pwd) +SKBENCH_DIR=${SKBENCH_DIR:-/home/schwarc/diplomovka/sk-bench} +MODEL_DIR=${MODEL_DIR:-/home/schwarc/diplomovka/skbench_evaluation/models/aya_300k_lr_0p00010} +OUTPUT_ROOT=${OUTPUT_ROOT:-/home/schwarc/diplomovka/skbench_evaluation/results} +GPU_ID=${GPU_ID:-1} +TASKS="$PROJECT/benchmarks/skbench/configs/tasks.txt" +OMITTED="$PROJECT/benchmarks/skbench/configs/omitted_tasks.txt" + +test -f "$MODEL_DIR/config.json" +test -f "$TASKS" +test -f "$OMITTED" +cd "$SKBENCH_DIR" + +RUN="$OUTPUT_ROOT/sk_zero_available_without_nli_lr_0p00010_$(date +%Y%m%d_%H%M%S)" +mkdir -p "$RUN" +cp "$TASKS" "$RUN/tasks.txt" +cp "$OMITTED" "$RUN/omitted_tasks.txt" +cp uv.lock "$RUN/uv.lock" +git rev-parse HEAD > "$RUN/skbench_commit.txt" + +CUDA_VISIBLE_DEVICES="$GPU_ID" VLLM_USE_FLASHINFER_SAMPLER=0 \ +uv run --locked python eval_vllm.py \ + --model-name "$MODEL_DIR" \ + --tasks-file "$RUN/tasks.txt" \ + --output-dir "$RUN" \ + --tensor-parallel-size 1 \ + --dtype bfloat16 \ + --gpu-memory-utilization 0.85 \ + --max-model-length 32768 \ + --max-batched-tokens 2048 \ + --enforce-eager \ + --save-details \ + 2>&1 | tee "$RUN/run.log" diff --git a/aya_300k_lr_experiments/scripts/run_aya_300k_lr_validation.sh b/aya_300k_lr_experiments/scripts/run_aya_300k_lr_validation.sh new file mode 100644 index 0000000..1c4efde --- /dev/null +++ b/aya_300k_lr_experiments/scripts/run_aya_300k_lr_validation.sh @@ -0,0 +1,76 @@ +#!/usr/bin/env bash +set -euo pipefail + +source /home/schwarc/miniconda3/etc/profile.d/conda.sh +conda activate llama_factory_unsloth + +ROOT="/home/schwarc/diplomovka" +EXP="$ROOT/aya_300k_lr_validation" + +cd /home/schwarc/LLaMA-Factory + +export CUDA_VISIBLE_DEVICES=0 +unset FORCE_TORCHRUN || true +export PYTHONUNBUFFERED=1 + + +RUNS=( + "lr_0p00018" + "lr_0p00020" + "lr_0p00023" +) + + +for NAME in "${RUNS[@]}"; do + + CFG="$EXP/configs/$NAME.yaml" + OUT="$EXP/$NAME" + LOG="$EXP/logs/$NAME.log" + DONE="$OUT/TRAINING_COMPLETE" + + echo + echo "========================================" + echo "RUN: $NAME" + echo "========================================" + + if [ -f "$DONE" ]; then + echo "Uz dokoncene - preskakujem." + continue + fi + + # Ak existuje checkpoint po predoslom pade servera, + # radsej automaticky nic neprepiseme. + if [ -d "$OUT" ] && \ + find "$OUT" -maxdepth 1 -type d \ + -name 'checkpoint-*' | grep -q .; then + + echo + echo "POZOR:" + echo "Nasiel som nedokonceny run s checkpointom:" + echo "$OUT" + echo + echo "Nebudem ho automaticky prepisovat." + echo "Treba ho najprv resume-nut." + exit 2 + fi + + + llamafactory-cli train "$CFG" \ + 2>&1 | tee "$LOG" + + mkdir -p "$OUT" + touch "$DONE" + + python \ + "$ROOT/scripts/analyze_aya_300k_lr.py" + +done + + +echo +echo "========================================" +echo "VSETKY 300K LR BEHY HOTOVE" +echo "========================================" + +python \ + "$ROOT/scripts/analyze_aya_300k_lr.py" diff --git a/aya_300k_lr_experiments/scripts/run_aya_300k_lr_validation_lower.sh b/aya_300k_lr_experiments/scripts/run_aya_300k_lr_validation_lower.sh new file mode 100644 index 0000000..5289159 --- /dev/null +++ b/aya_300k_lr_experiments/scripts/run_aya_300k_lr_validation_lower.sh @@ -0,0 +1,76 @@ +#!/usr/bin/env bash +set -euo pipefail + +source /home/schwarc/miniconda3/etc/profile.d/conda.sh +conda activate llama_factory_unsloth + +ROOT="/home/schwarc/diplomovka" +EXP="$ROOT/aya_300k_lr_validation" + +cd /home/schwarc/LLaMA-Factory + +export CUDA_VISIBLE_DEVICES=0 +unset FORCE_TORCHRUN || true +export PYTHONUNBUFFERED=1 + + +RUNS=( + "lr_0p00015" + "lr_0p00010" + "lr_0p00008" +) + + +for NAME in "${RUNS[@]}"; do + + CFG="$EXP/configs/$NAME.yaml" + OUT="$EXP/$NAME" + LOG="$EXP/logs/$NAME.log" + DONE="$OUT/TRAINING_COMPLETE" + + echo + echo "========================================" + echo "RUN: $NAME" + echo "========================================" + + if [ -f "$DONE" ]; then + echo "Uz dokoncene - preskakujem." + continue + fi + + # Ak existuje checkpoint po predoslom pade servera, + # radsej automaticky nic neprepiseme. + if [ -d "$OUT" ] && \ + find "$OUT" -maxdepth 1 -type d \ + -name 'checkpoint-*' | grep -q .; then + + echo + echo "POZOR:" + echo "Nasiel som nedokonceny run s checkpointom:" + echo "$OUT" + echo + echo "Nebudem ho automaticky prepisovat." + echo "Treba ho najprv resume-nut." + exit 2 + fi + + + llamafactory-cli train "$CFG" \ + 2>&1 | tee "$LOG" + + mkdir -p "$OUT" + touch "$DONE" + + python \ + "$ROOT/scripts/analyze_aya_300k_lr_all.py" + +done + + +echo +echo "========================================" +echo "VSETKY 300K LR BEHY HOTOVE" +echo "========================================" + +python \ + "$ROOT/scripts/analyze_aya_300k_lr_all.py" diff --git a/aya_300k_lr_experiments/scripts/run_lmeval_aya_300k_lr_0p00010.sh b/aya_300k_lr_experiments/scripts/run_lmeval_aya_300k_lr_0p00010.sh new file mode 100644 index 0000000..6ca0bcb --- /dev/null +++ b/aya_300k_lr_experiments/scripts/run_lmeval_aya_300k_lr_0p00010.sh @@ -0,0 +1,74 @@ +#!/usr/bin/env bash +set -euo pipefail + +eval "$(/home/schwarc/miniconda3/bin/conda shell.bash hook)" +conda activate lm_eval_hruska +cd /home/schwarc/lm-evaluation-harness + +# This directory already contains the base model merged with the Aya adapter. +MODEL="/home/schwarc/diplomovka/skbench_evaluation/models/aya_300k_lr_0p00010" +TASKS="arc_sk,hellaswag_sk,m_mmlu_sk,truthfulqa_sk_mc1,truthfulqa_sk_mc2,sklegal,skquad" +INCLUDE_PATH="/home/schwarc/diplomovka/lmeval_slovak_tasks" +ROOT="/home/schwarc/diplomovka/aya_300k_lr_validation/lmeval" +NAME="lr_0p00010_$(date +%Y%m%d_%H%M%S)" +OUT="$ROOT/results/$NAME" +LOG="$ROOT/logs/$NAME.log" +SUMMARY="$ROOT/summary.csv" + +test -f "$MODEL/config.json" +test -f "$MODEL/model.safetensors.index.json" +test -d "$INCLUDE_PATH" +mkdir -p "$OUT" "$ROOT/logs" +cp -a "$INCLUDE_PATH" "$OUT/custom_tasks" +git rev-parse HEAD > "$OUT/lm_eval_commit.txt" +printf '%s\n' "$TASKS" > "$OUT/tasks.txt" +printf '%s\n' "model=hf" "pretrained=$MODEL" "tokenizer=/home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4" "dtype=bfloat16" \ + "num_fewshot=0" "batch_size=1" "apply_chat_template=false" \ + "CUDA_VISIBLE_DEVICES=1" > "$OUT/run_settings.txt" + +python - <<'PY' > "$OUT/package_versions.json" +import importlib.metadata as md +import json + +versions = {} +for package in ("lm_eval", "transformers", "torch", "datasets", "peft", "evaluate"): + try: + versions[package] = md.version(package) + except md.PackageNotFoundError: + versions[package] = None +print(json.dumps(versions, indent=2)) +PY + +if [[ ! -f "$SUMMARY" ]]; then + printf '%s\n' 'model,exit_code,start_time,end_time,log_file' > "$SUMMARY" +fi + +START_TIME=$(date '+%Y-%m-%d %H:%M:%S') +printf 'Model: %s\nResults: %s\nLog: %s\n' "$MODEL" "$OUT" "$LOG" + +set +e +CUDA_VISIBLE_DEVICES=1 lm_eval \ + --model hf \ + --model_args "pretrained=$MODEL,tokenizer=/home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4,dtype=bfloat16" \ + --tasks "$TASKS" \ + --include_path "$INCLUDE_PATH" \ + --device cuda:0 \ + --batch_size 1 \ + --num_fewshot 0 \ + --output_path "$OUT" \ + 2>&1 | tee "$LOG" +EXIT_CODES=("${PIPESTATUS[@]}") +set -e +EXIT_CODE=${EXIT_CODES[0]} +if (( EXIT_CODE == 0 )); then + EXIT_CODE=${EXIT_CODES[1]} +fi + +END_TIME=$(date '+%Y-%m-%d %H:%M:%S') +printf '%s,%s,%s,%s,%s\n' "$NAME" "$EXIT_CODE" "$START_TIME" "$END_TIME" "$LOG" >> "$SUMMARY" +if (( EXIT_CODE == 0 )); then + printf 'Evaluation completed: %s\n' "$OUT" +else + printf 'Evaluation failed (exit code %s). Log: %s\n' "$EXIT_CODE" "$LOG" >&2 +fi +exit "$EXIT_CODE" diff --git a/aya_300k_lr_experiments/scripts/setup_aya_300k_lr_validation.sh b/aya_300k_lr_experiments/scripts/setup_aya_300k_lr_validation.sh new file mode 100644 index 0000000..d8eb396 --- /dev/null +++ b/aya_300k_lr_experiments/scripts/setup_aya_300k_lr_validation.sh @@ -0,0 +1,899 @@ +#!/usr/bin/env bash +set -euo pipefail + +ROOT="/home/schwarc/diplomovka" +LF="/home/schwarc/LLaMA-Factory" + +EXP="$ROOT/aya_300k_lr_validation" +CONFIG_DIR="$EXP/configs" +LOG_DIR="$EXP/logs" +RESULT_DIR="$EXP/results" + +DATA_FILE="$LF/data/aya_slovak_300k_fixed.jsonl" +DATASET_INFO="$LF/data/dataset_info.json" + +BASE_CONFIG="$ROOT/optuna_lr_hpo_1gpu/base_hpo_config.yaml" + +mkdir -p "$EXP" "$CONFIG_DIR" "$LOG_DIR" "$RESULT_DIR" "$ROOT/scripts" + +source /home/schwarc/miniconda3/etc/profile.d/conda.sh +conda activate llama_factory_unsloth + + +echo +echo "========================================" +echo "1. KONTROLA PROSTREDIA" +echo "========================================" + +python - <<'PY' +import torch +import datasets +import yaml + +print("Torch:", torch.__version__) +print("CUDA:", torch.cuda.is_available()) +print("Datasets:", datasets.__version__) + +if torch.cuda.is_available(): + print("GPU 0:", torch.cuda.get_device_name(0)) +PY + + +# ============================================================ +# 2. VYTVORENIE FIXNEHO 300K DATASETU +# ============================================================ + +echo +echo "========================================" +echo "2. AYA SLOVAK 300K" +echo "========================================" + +python - <<'PY' +from pathlib import Path +from datasets import load_dataset +import json + +TARGET = Path( + "/home/schwarc/LLaMA-Factory/data/" + "aya_slovak_300k_fixed.jsonl" +) + +EXPECTED = 300_000 +SEED = 42 + + +def count_lines(path): + with path.open("r", encoding="utf-8") as f: + return sum(1 for _ in f) + + +if TARGET.exists(): + count = count_lines(TARGET) + + if count == EXPECTED: + print( + f"Dataset uz existuje a ma spravne " + f"{count} riadkov." + ) + raise SystemExit(0) + + else: + raise RuntimeError( + f"{TARGET} existuje, ale ma " + f"{count} riadkov namiesto {EXPECTED}. " + "Nebudem ho automaticky prepisovat." + ) + + +print("Nacitavam Aya Collection - slovak/train...") + +ds = load_dataset( + "CohereLabs/aya_collection_language_split", + "slovak", + split="train", +) + +print("Povodny train split:", len(ds)) + + +def valid_batch(batch): + result = [] + + for inp, target in zip( + batch["inputs"], + batch["targets"], + ): + ok = ( + isinstance(inp, str) + and isinstance(target, str) + and bool(inp.strip()) + and bool(target.strip()) + ) + + result.append(ok) + + return result + + +print("Filtrujem prazdne inputs/targets...") + +ds = ds.filter( + valid_batch, + batched=True, + batch_size=10000, + num_proc=4, +) + +print("Platne zaznamy:", len(ds)) + +if len(ds) < EXPECTED: + raise RuntimeError( + f"Po filtrovani ostalo iba {len(ds)} zaznamov." + ) + + +print("Shuffle seed =", SEED) + +ds = ds.shuffle(seed=SEED) + +ds = ds.select( + range(EXPECTED) +) + + +print("Zapisujem:", TARGET) + +with TARGET.open( + "w", + encoding="utf-8" +) as f: + + for row in ds: + + item = { + "instruction": row["inputs"].strip(), + "input": "", + "output": row["targets"].strip(), + } + + f.write( + json.dumps( + item, + ensure_ascii=False + ) + + "\n" + ) + + +count = count_lines(TARGET) + +if count != EXPECTED: + raise RuntimeError( + f"Finalny dataset ma {count} riadkov." + ) + +print("HOTOVO.") +print("Pocet:", count) +print("Dataset:", TARGET) +PY + + +# ============================================================ +# 3. DATASET_INFO.JSON +# ============================================================ + +echo +echo "========================================" +echo "3. DATASET_INFO" +echo "========================================" + +python - <<'PY' +from pathlib import Path +import json +import copy +import shutil + +p = Path( + "/home/schwarc/LLaMA-Factory/data/" + "dataset_info.json" +) + +backup = p.parent / ( + "dataset_info_before_aya300k_backup.json" +) + +if not backup.exists(): + shutil.copy2(p, backup) + + +with p.open( + "r", + encoding="utf-8" +) as f: + info = json.load(f) + + +source_name = "aya_slovak_100k_fixed" +new_name = "aya_slovak_300k_fixed" + +if source_name not in info: + raise RuntimeError( + f"V dataset_info chyba {source_name}" + ) + + +entry = copy.deepcopy( + info[source_name] +) + +entry["file_name"] = ( + "aya_slovak_300k_fixed.jsonl" +) + +info[new_name] = entry + + +with p.open( + "w", + encoding="utf-8" +) as f: + + json.dump( + info, + f, + ensure_ascii=False, + indent=2 + ) + + +print( + "Registrovany dataset:", + new_name +) + +print( + json.dumps( + info[new_name], + ensure_ascii=False, + indent=2 + ) +) +PY + + +# ============================================================ +# 4. VYTVORENIE 3 CONFIGOV +# ============================================================ + +echo +echo "========================================" +echo "4. CONFIGY" +echo "========================================" + +python - <<'PY' +from pathlib import Path +import yaml + +ROOT = Path( + "/home/schwarc/diplomovka" +) + +EXP = ROOT / "aya_300k_lr_validation" + +BASE = ( + ROOT + / "optuna_lr_hpo_1gpu" + / "base_hpo_config.yaml" +) + +if not BASE.exists(): + BASE = ( + ROOT + / "configs" + / "hruska_compare9_100k_fixed" + / "10_r16_a32_lr1e4.yaml" + ) + +if not BASE.exists(): + raise RuntimeError( + "Nenasiel som base config." + ) + + +with BASE.open( + "r", + encoding="utf-8" +) as f: + base = yaml.safe_load(f) + + +# ------------------------------------------------------------ +# FIXNE PARAMETRE PRE VSETKY 3 BEHY +# ------------------------------------------------------------ + +base["dataset"] = "aya_slovak_300k_fixed" +base["streaming"] = False + +# presne 1 cely epoch +base.pop("max_steps", None) +base["num_train_epochs"] = 1.0 + +# 300k -> 1000 validation -> cca 299k train +base["val_size"] = 1000 + +# 1 GPU: +# 1 * 16 * 1 = effective batch 16 +base["per_device_train_batch_size"] = 1 +base["per_device_eval_batch_size"] = 1 +base["gradient_accumulation_steps"] = 16 + +# cca 18 688 optimizer krokov +# ~8 % warmup +base["warmup_steps"] = 1500 +base.pop("warmup_ratio", None) + +# chceme sledovat priebeh +base["eval_strategy"] = "steps" +base["eval_steps"] = 1000 +base["logging_steps"] = 10 + +# checkpointy pre pripad restartu servera +base["save_steps"] = 2000 +base["save_total_limit"] = 2 + +base["seed"] = 42 + +base["overwrite_output_dir"] = True +base["report_to"] = "none" + +# explicitne zachovame Unsloth + bf16 +base["use_unsloth"] = True +base["use_unsloth_gc"] = True +base["bf16"] = True +base["fp16"] = False + +# chceme rovnaky optimizer/scheduler +base["optim"] = "adamw_torch" +base["lr_scheduler_type"] = "cosine" + +# rovnaka gradient clipping hranica +base["max_grad_norm"] = 0.5 + + +runs = [ + ("lr_0p00018", 0.00018), + ("lr_0p00020", 0.00020), + ("lr_0p00023", 0.00023), +] + + +for name, lr in runs: + + cfg = dict(base) + + cfg["learning_rate"] = lr + + cfg["output_dir"] = str( + EXP / name + ) + + path = ( + EXP + / "configs" + / f"{name}.yaml" + ) + + with path.open( + "w", + encoding="utf-8" + ) as f: + + yaml.safe_dump( + cfg, + f, + sort_keys=False, + allow_unicode=True + ) + + print() + print(path) + print(" LR:", lr) + print( + " output:", + cfg["output_dir"] + ) + + +print() +print("Base kontrola:") +print("dataset:", base["dataset"]) +print("epochs:", base["num_train_epochs"]) +print( + "batch:", + base["per_device_train_batch_size"] +) +print( + "grad_accum:", + base["gradient_accumulation_steps"] +) +print( + "warmup:", + base["warmup_steps"] +) +print( + "eval_steps:", + base["eval_steps"] +) +print( + "save_steps:", + base["save_steps"] +) +print( + "LoRA rank:", + base.get("lora_rank") +) +print( + "LoRA alpha:", + base.get("lora_alpha") +) +print( + "LoRA dropout:", + base.get("lora_dropout") +) +print( + "LoRA target:", + base.get("lora_target") +) +PY + + +# ============================================================ +# 5. ANALYZATOR +# ============================================================ + +cat > "$ROOT/scripts/analyze_aya_300k_lr.py" <<'PY' +from pathlib import Path +import csv +import json +import math + + +ROOT = Path( + "/home/schwarc/diplomovka/" + "aya_300k_lr_validation" +) + +RESULT_DIR = ROOT / "results" +RESULT_DIR.mkdir( + parents=True, + exist_ok=True +) + + +RUNS = [ + ("0.00018", ROOT / "lr_0p00018"), + ("0.00020", ROOT / "lr_0p00020"), + ("0.00023", ROOT / "lr_0p00023"), +] + + +def load_best_state(output): + + files = list( + output.rglob( + "trainer_state.json" + ) + ) + + if not files: + return None + + candidates = [] + + for p in files: + try: + data = json.loads( + p.read_text( + encoding="utf-8" + ) + ) + + candidates.append( + ( + int( + data.get( + "global_step", + 0 + ) + ), + data, + p, + ) + ) + + except Exception: + pass + + if not candidates: + return None + + return max( + candidates, + key=lambda x: x[0] + ) + + +rows = [] +eval_curves = {} +grad_curves = {} + + +for lr, output in RUNS: + + result = load_best_state( + output + ) + + if result is None: + continue + + global_step, state, state_file = result + + history = state.get( + "log_history", + [] + ) + + evals = [] + grads = [] + + for item in history: + + step = int( + item.get("step", 0) + ) + + if "eval_loss" in item: + try: + value = float( + item["eval_loss"] + ) + if math.isfinite(value): + evals.append( + (step, value) + ) + except Exception: + pass + + if "grad_norm" in item: + try: + value = float( + item["grad_norm"] + ) + if math.isfinite(value): + grads.append( + (step, value) + ) + except Exception: + pass + + + if evals: + final_eval_step, final_eval = ( + evals[-1] + ) + + min_eval_step, min_eval = min( + evals, + key=lambda x: x[1] + ) + else: + final_eval_step = "" + final_eval = "" + min_eval_step = "" + min_eval = "" + + + grad_values = [ + value + for _, value in grads + ] + + if grad_values: + + max_grad = max( + grad_values + ) + + ordered = sorted( + grad_values + ) + + p95_index = int( + 0.95 + * (len(ordered) - 1) + ) + + p95_grad = ( + ordered[p95_index] + ) + + else: + max_grad = "" + p95_grad = "" + + + rows.append( + { + "learning_rate": lr, + "global_step": global_step, + "final_eval_step": + final_eval_step, + "final_eval_loss": + final_eval, + "min_eval_step": + min_eval_step, + "min_eval_loss": + min_eval, + "max_grad_norm": + max_grad, + "p95_grad_norm": + p95_grad, + "trainer_state": + str(state_file), + } + ) + + eval_curves[lr] = evals + grad_curves[lr] = grads + + +csv_path = ( + RESULT_DIR + / "aya_300k_lr_summary.csv" +) + +with csv_path.open( + "w", + encoding="utf-8", + newline="" +) as f: + + fields = [ + "learning_rate", + "global_step", + "final_eval_step", + "final_eval_loss", + "min_eval_step", + "min_eval_loss", + "max_grad_norm", + "p95_grad_norm", + "trainer_state", + ] + + writer = csv.DictWriter( + f, + fieldnames=fields + ) + + writer.writeheader() + writer.writerows(rows) + + +print() +print("===== 300K LR SUMMARY =====") + +for row in rows: + print( + f"LR={row['learning_rate']} | " + f"step={row['global_step']} | " + f"final_eval={row['final_eval_loss']} | " + f"min_eval={row['min_eval_loss']} " + f"@ {row['min_eval_step']} | " + f"max_grad={row['max_grad_norm']} | " + f"p95_grad={row['p95_grad_norm']}" + ) + +print() +print("CSV:", csv_path) + + +try: + import matplotlib.pyplot as plt + + if eval_curves: + + plt.figure( + figsize=(12, 7) + ) + + for lr, points in ( + eval_curves.items() + ): + if not points: + continue + + plt.plot( + [x for x, _ in points], + [y for _, y in points], + marker="o", + label=f"LR {lr}", + ) + + plt.xlabel( + "optimizer step" + ) + plt.ylabel( + "eval loss" + ) + plt.title( + "Aya 300k - eval loss" + ) + plt.legend() + plt.grid(alpha=0.25) + plt.tight_layout() + + plt.savefig( + RESULT_DIR + / "eval_loss_over_steps.png", + dpi=200, + bbox_inches="tight" + ) + + plt.close() + + + if grad_curves: + + plt.figure( + figsize=(12, 7) + ) + + for lr, points in ( + grad_curves.items() + ): + if not points: + continue + + plt.plot( + [x for x, _ in points], + [y for _, y in points], + label=f"LR {lr}", + ) + + plt.xlabel( + "optimizer step" + ) + plt.ylabel( + "grad norm" + ) + plt.title( + "Aya 300k - gradient norm" + ) + plt.yscale("log") + plt.legend() + plt.grid(alpha=0.25) + plt.tight_layout() + + plt.savefig( + RESULT_DIR + / "grad_norm_over_steps.png", + dpi=200, + bbox_inches="tight" + ) + + plt.close() + + +except Exception as exc: + print( + "Grafy sa nepodarilo vytvorit:", + exc + ) +PY + + +# ============================================================ +# 6. RUNNER +# ============================================================ + +cat > "$ROOT/scripts/run_aya_300k_lr_validation.sh" <<'BASH2' +#!/usr/bin/env bash +set -euo pipefail + +source /home/schwarc/miniconda3/etc/profile.d/conda.sh +conda activate llama_factory_unsloth + +ROOT="/home/schwarc/diplomovka" +EXP="$ROOT/aya_300k_lr_validation" + +cd /home/schwarc/LLaMA-Factory + +export CUDA_VISIBLE_DEVICES=0 +unset FORCE_TORCHRUN || true +export PYTHONUNBUFFERED=1 + + +RUNS=( + "lr_0p00018" + "lr_0p00020" + "lr_0p00023" +) + + +for NAME in "${RUNS[@]}"; do + + CFG="$EXP/configs/$NAME.yaml" + OUT="$EXP/$NAME" + LOG="$EXP/logs/$NAME.log" + DONE="$OUT/TRAINING_COMPLETE" + + echo + echo "========================================" + echo "RUN: $NAME" + echo "========================================" + + if [ -f "$DONE" ]; then + echo "Uz dokoncene - preskakujem." + continue + fi + + # Ak existuje checkpoint po predoslom pade servera, + # radsej automaticky nic neprepiseme. + if [ -d "$OUT" ] && \ + find "$OUT" -maxdepth 1 -type d \ + -name 'checkpoint-*' | grep -q .; then + + echo + echo "POZOR:" + echo "Nasiel som nedokonceny run s checkpointom:" + echo "$OUT" + echo + echo "Nebudem ho automaticky prepisovat." + echo "Treba ho najprv resume-nut." + exit 2 + fi + + + llamafactory-cli train "$CFG" \ + 2>&1 | tee "$LOG" + + mkdir -p "$OUT" + touch "$DONE" + + python \ + "$ROOT/scripts/analyze_aya_300k_lr.py" + +done + + +echo +echo "========================================" +echo "VSETKY 300K LR BEHY HOTOVE" +echo "========================================" + +python \ + "$ROOT/scripts/analyze_aya_300k_lr.py" +BASH2 + + +chmod +x \ +"$ROOT/scripts/run_aya_300k_lr_validation.sh" + + +echo +echo "========================================" +echo "SETUP HOTOVY" +echo "========================================" + +echo +echo "Dataset:" +wc -l "$DATA_FILE" + +echo +echo "Configy:" +ls -1 "$CONFIG_DIR" + +echo +echo "Runner:" +echo "$ROOT/scripts/run_aya_300k_lr_validation.sh" diff --git a/aya_300k_lr_experiments/scripts/unpack_results_archive.py b/aya_300k_lr_experiments/scripts/unpack_results_archive.py new file mode 100644 index 0000000..406c856 --- /dev/null +++ b/aya_300k_lr_experiments/scripts/unpack_results_archive.py @@ -0,0 +1,74 @@ +from pathlib import Path, PurePosixPath +import argparse +import collections +import hashlib +import json +import re +import tarfile + +parser = argparse.ArgumentParser(description="Safely extract an experiment archive and create audit path manifests (Python 3.11+).") +parser.add_argument("--archive", type=Path, required=True) +parser.add_argument("--output", type=Path, required=True) +args = parser.parse_args() +SOURCE = args.archive.resolve() +DEST = args.output.resolve() +if DEST.exists() and any(DEST.iterdir()): + raise SystemExit(f"Output directory must be empty: {DEST}") +DEST.mkdir(parents=True, exist_ok=True) +start_size = SOURCE.stat().st_size + +def safe_component(value): + value = value.replace('%', '%25') + value = re.sub(r'[<>:"\\|?*\x00-\x1f]', lambda m: f'%{ord(m.group()):02X}', value) + while value.endswith((' ', '.')): + value = value[:-1] + f'%{ord(value[-1]):02X}' + if value.split('.')[0].upper() in {'CON', 'PRN', 'AUX', 'NUL', *(f'COM{i}' for i in range(1, 10)), *(f'LPT{i}' for i in range(1, 10))}: + value = '%5F' + value + return value + +manifest = [] +seen = set() +with tarfile.open(SOURCE, 'r:gz') as archive: + for member in archive: + original = PurePosixPath(member.name) + if original.is_absolute() or '..' in original.parts or '\\' in member.name: + raise RuntimeError(f'Unsafe archive path: {member.name!r}') + relative = Path(*(safe_component(x) for x in original.parts if x not in ('', '.'))) + target = DEST / relative + if not target.resolve().is_relative_to(DEST.resolve()): + raise RuntimeError(f'Path escapes extraction root: {member.name!r}') + row = {'original': member.name, 'local': relative.as_posix(), 'size': member.size, 'type': 'other'} + if member.isdir(): + target.mkdir(parents=True, exist_ok=True) + row['type'] = 'directory' + elif member.isfile(): + key = relative.as_posix().casefold() + if key in seen: + raise RuntimeError(f'Archive filename collision: {member.name!r}') + seen.add(key) + target.parent.mkdir(parents=True, exist_ok=True) + extracted = archive.extractfile(member) + assert extracted is not None + with extracted, target.open('wb') as handle: + while chunk := extracted.read(1024 * 1024): + handle.write(chunk) + if target.stat().st_size != member.size: + raise RuntimeError(f'Incomplete extraction: {member.name!r}') + row['type'] = 'file' + else: + row['type'] = 'skipped_link_or_special' + manifest.append(row) + +if SOURCE.stat().st_size != start_size: + raise RuntimeError('Archive changed during extraction') +with SOURCE.open('rb') as handle: + digest = hashlib.file_digest(handle, 'sha256').hexdigest() +(DEST / 'archive_manifest.json').write_text(json.dumps(manifest, ensure_ascii=False, indent=2), encoding='utf-8') +files = [x for x in manifest if x['type'] == 'file'] +summary = {'source': str(SOURCE), 'compressed_bytes': start_size, 'sha256': digest, + 'files': len(files), 'uncompressed_bytes': sum(x['size'] for x in files), + 'skipped_special': [x for x in manifest if x['type'] == 'skipped_link_or_special'], + 'top_level_files': dict(collections.Counter(PurePosixPath(x['original']).parts[0] for x in files)), + 'largest_files': sorted(files, key=lambda x: x['size'], reverse=True)[:12]} +(DEST / 'archive_summary.json').write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding='utf-8') +print(json.dumps(summary, ensure_ascii=False, indent=2)) diff --git a/aya_300k_lr_experiments/trials/lr_0p00008/TRAINING_COMPLETE b/aya_300k_lr_experiments/trials/lr_0p00008/TRAINING_COMPLETE new file mode 100644 index 0000000..e69de29 diff --git a/aya_300k_lr_experiments/trials/lr_0p00008/adapter_config.json b/aya_300k_lr_experiments/trials/lr_0p00008/adapter_config.json new file mode 100644 index 0000000..b2ed561 --- /dev/null +++ b/aya_300k_lr_experiments/trials/lr_0p00008/adapter_config.json @@ -0,0 +1,50 @@ +{ + "alora_invocation_tokens": null, + "alpha_pattern": {}, + "arrow_config": null, + "auto_mapping": { + "base_model_class": "MistralForCausalLM", + "parent_library": "transformers.models.mistral.modeling_mistral", + "unsloth_fixed": true + }, + "base_model_name_or_path": "slovak-nlp/mistral-sk-7b", + "bias": "none", + "corda_config": null, + "ensure_weight_tying": false, + "eva_config": null, + "exclude_modules": null, + "fan_in_fan_out": false, + "inference_mode": true, + "init_lora_weights": true, + "layer_replication": null, + "layers_pattern": null, + "layers_to_transform": null, + "loftq_config": {}, + "lora_alpha": 32, + "lora_bias": false, + "lora_dropout": 0.0, + "megatron_config": null, + "megatron_core": "megatron.core", + "modules_to_save": null, + "peft_type": "LORA", + "peft_version": "0.18.1", + "qalora_group_size": 16, + "r": 16, + "rank_pattern": {}, + "revision": null, + "target_modules": [ + "q_proj", + "o_proj", + "gate_proj", + "k_proj", + "up_proj", + "v_proj", + "down_proj" + ], + "target_parameters": null, + "task_type": "CAUSAL_LM", + "trainable_token_indices": null, + "use_dora": false, + "use_qalora": false, + "use_rslora": false +} \ No newline at end of file diff --git a/aya_300k_lr_experiments/trials/lr_0p00008/all_results.json b/aya_300k_lr_experiments/trials/lr_0p00008/all_results.json new file mode 100644 index 0000000..9753bf2 --- /dev/null +++ b/aya_300k_lr_experiments/trials/lr_0p00008/all_results.json @@ -0,0 +1,13 @@ +{ + "epoch": 1.0, + "eval_loss": 0.875216543674469, + "eval_runtime": 84.3566, + "eval_samples_per_second": 11.854, + "eval_steps_per_second": 11.854, + "num_input_tokens_seen": 62891360, + "total_flos": 2.69902163257983e+18, + "train_loss": 0.9401782060884041, + "train_runtime": 79243.6449, + "train_samples_per_second": 3.773, + "train_steps_per_second": 0.236 +} \ No newline at end of file diff --git a/aya_300k_lr_experiments/trials/lr_0p00008/eval_results.json b/aya_300k_lr_experiments/trials/lr_0p00008/eval_results.json new file mode 100644 index 0000000..116ee66 --- /dev/null +++ b/aya_300k_lr_experiments/trials/lr_0p00008/eval_results.json @@ -0,0 +1,8 @@ +{ + "epoch": 1.0, + "eval_loss": 0.875216543674469, + "eval_runtime": 84.3566, + "eval_samples_per_second": 11.854, + "eval_steps_per_second": 11.854, + "num_input_tokens_seen": 62891360 +} \ No newline at end of file diff --git a/aya_300k_lr_experiments/trials/lr_0p00008/train_results.json b/aya_300k_lr_experiments/trials/lr_0p00008/train_results.json new file mode 100644 index 0000000..28d209e --- /dev/null +++ b/aya_300k_lr_experiments/trials/lr_0p00008/train_results.json @@ -0,0 +1,9 @@ +{ + "epoch": 1.0, + "num_input_tokens_seen": 62891360, + "total_flos": 2.69902163257983e+18, + "train_loss": 0.9401782060884041, + "train_runtime": 79243.6449, + "train_samples_per_second": 3.773, + "train_steps_per_second": 0.236 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