diff --git a/README.md b/README.md
index 357f2d8..8a68024 100644
--- a/README.md
+++ b/README.md
@@ -871,7 +871,7 @@ Presné hodnoty LR, finálne aj minimálne eval loss, kroky, training loss a run
# Hruška – hľadanie learning rate na 300k Aya 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.
+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**.
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).
@@ -898,7 +898,7 @@ Zo slovenského `train` splitu `CohereLabs/aya_collection_language_split` sa vyf
| 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.
+Pôvodné [konfigurácie](aya_300k_lr_experiments/configs/) a [tréningové logy](aya_300k_lr_experiments/logs/) sú zachované.
## Výsledky a výber LR pre ďalší tréning
@@ -913,34 +913,26 @@ Prvá séria obsahovala LR `0.00018`, `0.00020` a `0.00023`. Po jej vyhodnotení
| 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.
+**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.
-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.
+Na 30k skúške bol najlepší skúšaný LR `0.0002`. Výsledky 300k série preto ukazujú význam overenia LR na väčšej vzorke.
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).
-
- 
-
-
- 
-
## 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`.
+Na následné benchmarky sa vybral model s LR `0.00010`. Jeho LoRA adaptér sa zlúčil s pôvodným základným modelom do samostatného modelu v `bfloat16`.
-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.
+Benchmarky skúmajú schopnosti tohto vybraného modelu. **Ostatných päť LR zatiaľ týmito benchmarkmi nebolo porovnaných**.
### 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 |
@@ -950,19 +942,16 @@ Vyhodnotenie dokončilo všetkých sedem pôvodných úloh a skončilo s návrat
| 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).
+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.
### 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.
+Dokončil sa **čiastkový slovenský zero-shot beh s 222 položkami**. 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.
+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`. 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).
+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.**
-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 (%) |
|---|---|---:|---:|
@@ -981,14 +970,8 @@ Výber opisných výsledkov z dostupných úloh. Uvedený priemer je vážený p
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.
+**Tento výsledok nie je oficiálne celkové skóre úplného SK-bench.**
-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.
+Pri príprave sa sampler FlashInfer pokúšal kompilovať cez starší systémový CUDA toolkit. Úspešný beh používal `VLLM_USE_FLASHINFER_SAMPLER=0` a `--enforce-eager`.
-## 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/benchmarks/export/chat_template.jinja b/aya_300k_lr_experiments/benchmarks/export/chat_template.jinja
deleted file mode 100644
index 8d7a085..0000000
--- a/aya_300k_lr_experiments/benchmarks/export/chat_template.jinja
+++ /dev/null
@@ -1,9 +0,0 @@
-{% 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
deleted file mode 100644
index 13eab00..0000000
--- a/aya_300k_lr_experiments/benchmarks/export/config.json
+++ /dev/null
@@ -1,30 +0,0 @@
-{
- "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
deleted file mode 100644
index fcbcfa7..0000000
--- a/aya_300k_lr_experiments/benchmarks/export/export_lr_0p00010.yaml
+++ /dev/null
@@ -1,12 +0,0 @@
-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
deleted file mode 100644
index b7715d4..0000000
--- a/aya_300k_lr_experiments/benchmarks/export/generation_config.json
+++ /dev/null
@@ -1,6 +0,0 @@
-{
- "_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
deleted file mode 100644
index 6a1eb78..0000000
--- a/aya_300k_lr_experiments/benchmarks/export/tokenizer_config.json
+++ /dev/null
@@ -1,19 +0,0 @@
-{
- "add_prefix_space": true,
- "backend": "tokenizers",
- "bos_token": "",
- "clean_up_tokenization_spaces": false,
- "eos_token": "",
- "extra_special_tokens": [],
- "is_local": false,
- "legacy": true,
- "model_max_length": 1000000000000000019884624838656,
- "pad_token": "",
- "padding_side": "left",
- "sp_model_kwargs": {},
- "spaces_between_special_tokens": false,
- "split_special_tokens": false,
- "tokenizer_class": "TokenizersBackend",
- "unk_token": "",
- "use_default_system_prompt": false
-}
diff --git a/aya_300k_lr_experiments/benchmarks/lmeval/provenance/SHA256SUMS.txt b/aya_300k_lr_experiments/benchmarks/lmeval/provenance/SHA256SUMS.txt
deleted file mode 100644
index 79a049e..0000000
--- a/aya_300k_lr_experiments/benchmarks/lmeval/provenance/SHA256SUMS.txt
+++ /dev/null
@@ -1,18 +0,0 @@
-499e639455dc6bb9c79228b54b82c66b509f8fe6f2d33513b482b90dbf990674 comparison_with_100k.csv
-b587953b0ce136fea20edf50105005c645a2abfad2cd4f6bfa58ee17cf36b34c full_metrics.csv
-fd33cc64e660d4d996bd1b6af966e900c72b0c3b5c4bdb4e6915ac0756814e94 logs/lr_0p00010_20261023_224332.log
-2e959d2b2052ecc4dc5f571ac674ccaa39119149c58e6ac0da467aa0335b8248 provenance/comparability_audit.json
-f491f734663154901f0b5a4bb44aaed69b5d95448415291107fea0c703b055e8 provenance/custom_tasks/arc_sk.yaml
-f2feb3ae7dc6e8ae3db09a91747119d7256d1ef33b9a1e417a40cb2a949ce5b9 provenance/custom_tasks/hellaswag_sk.yaml
-d2f8c18839d15d365eed6997564e11aab43173eef91470e8a5834a9471772c2a provenance/custom_tasks/m_mmlu_sk.yaml
-9443e2d5e398c82f921d8e714ff01693bfce793eef9bf3b0ad581a8bbf137c4a provenance/custom_tasks/sklegal.yaml
-759c8cb35d27e50dd4dc4e81b34932d8bc96e36e309ed666858a904624a61131 provenance/custom_tasks/skquad.yaml
-de1b8b08f998ebb3dcdd881c3e2aaba89dd53f6ea2a51b0529eadf7936ef2ed3 provenance/custom_tasks/task.py
-fa1e7075c4715a4465be66041d36ef3f9b4dc8a7e41898554078294d2510f525 provenance/custom_tasks/task_configs_from_wandb.json
-cf922b04a755762060430ef37a18a860017cc9c79e89cf23ac7465fa97244faa provenance/custom_tasks/truthfulqa_sk_mc1.yaml
-b9209889fdf0e38915e29b83911213010f6c49d474836f474491da085ab26e01 provenance/custom_tasks/truthfulqa_sk_mc2.yaml
-205bd6b1be478ed9cb2a104bf47214cf75debe485fb43c6b78d6584e638283de provenance/lm_eval_commit.txt
-b4dcc9d0c2e7b5f5036ddc38a7aad4111934e39d84019b2122ad6561b780b5a4 provenance/package_versions.json
-44d8afff7e8d918ef9e53404a8fa17fe19d6dcfaf782519f01be44a2408da898 provenance/run_settings.txt
-f6f8d8470b78daf35d80f5a7315cc9f38d867daaead08df39a165cbcf98afd4e provenance/tasks.txt
-5774cad7440ab807be44dc5bfa82ac7cbb4c0281204fc397a10c2bbfe7b8f452 results/results_2026-10-24T02-28-32.987157.json
diff --git a/aya_300k_lr_experiments/benchmarks/lmeval/provenance/comparability_audit.json b/aya_300k_lr_experiments/benchmarks/lmeval/provenance/comparability_audit.json
deleted file mode 100644
index f2100e0..0000000
--- a/aya_300k_lr_experiments/benchmarks/lmeval/provenance/comparability_audit.json
+++ /dev/null
@@ -1,379 +0,0 @@
-{
- "comparison_scope": "All ten Hruska Aya 100k runs; Titan excluded.",
- "units": "Accuracy metrics use 0..1; SKQuad scores use 0..100. N/A remains unavailable.",
- "new_result_sha256": "5774cad7440ab807be44dc5bfa82ac7cbb4c0281204fc397a10c2bbfe7b8f452",
- "task_hashes_caveat": "task_hashes is empty in all eleven JSONs; equality is not a nonempty content-hash verification.",
- "model_loading_difference": "Older runs load base model plus PEFT adapter; 300k loads a merged model with explicit base tokenizer. Model arguments, parameter counts and model SHA therefore differ.",
- "new_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
- }
- },
- "comparisons": [
- {
- "model_id": "01_baseline_r16_a16_lr5e5_adamw",
- "equal_to_300k": {
- "versions": true,
- "configs": true,
- "task_hashes": true,
- "n-samples": true,
- "n-shot": true,
- "git_hash": true,
- "transformers_version": true,
- "chat_template": true,
- "chat_template_sha": true,
- "system_instruction": true,
- "fewshot_as_multiturn": true
- },
- "evaluation_config_differences": {
- "model_args": {
- "older_100k": "pretrained=slovak-nlp/mistral-sk-7b,peft=/home/schwarc/diplomovka/hruska_compare9_100k_fixed_01_baseline_r16_a16_lr5e5_adamw,dtype=bfloat16",
- "new_300k": "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": {
- "older_100k": 7283675136,
- "new_300k": 7241732096
- },
- "model_sha": {
- "older_100k": "089497ae0d72018e9591895c54f774bf0b4a83a4",
- "new_300k": ""
- },
- "peft_sha": {
- "older_100k": "",
- "new_300k": null
- }
- }
- },
- {
- "model_id": "02_optimizer_adamw8bit_r16_a16_lr5e5",
- "equal_to_300k": {
- "versions": true,
- "configs": true,
- "task_hashes": true,
- "n-samples": true,
- "n-shot": true,
- "git_hash": true,
- "transformers_version": true,
- "chat_template": true,
- "chat_template_sha": true,
- "system_instruction": true,
- "fewshot_as_multiturn": true
- },
- "evaluation_config_differences": {
- "model_args": {
- "older_100k": "pretrained=slovak-nlp/mistral-sk-7b,peft=/home/schwarc/diplomovka/hruska_compare9_100k_fixed_02_optimizer_adamw8bit_r16_a16_lr5e5,dtype=bfloat16",
- "new_300k": "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": {
- "older_100k": 7283675136,
- "new_300k": 7241732096
- },
- "model_sha": {
- "older_100k": "089497ae0d72018e9591895c54f774bf0b4a83a4",
- "new_300k": ""
- },
- "peft_sha": {
- "older_100k": "",
- "new_300k": null
- }
- }
- },
- {
- "model_id": "03_rank8_a16_lr5e5",
- "equal_to_300k": {
- "versions": true,
- "configs": true,
- "task_hashes": true,
- "n-samples": true,
- "n-shot": true,
- "git_hash": true,
- "transformers_version": true,
- "chat_template": true,
- "chat_template_sha": true,
- "system_instruction": true,
- "fewshot_as_multiturn": true
- },
- "evaluation_config_differences": {
- "model_args": {
- "older_100k": "pretrained=slovak-nlp/mistral-sk-7b,peft=/home/schwarc/diplomovka/hruska_compare9_100k_fixed_03_rank8_a16_lr5e5,dtype=bfloat16",
- "new_300k": "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": {
- "older_100k": 7262703616,
- "new_300k": 7241732096
- },
- "model_sha": {
- "older_100k": "089497ae0d72018e9591895c54f774bf0b4a83a4",
- "new_300k": ""
- },
- "peft_sha": {
- "older_100k": "",
- "new_300k": null
- }
- }
- },
- {
- "model_id": "04_rank32_a32_lr5e5",
- "equal_to_300k": {
- "versions": true,
- "configs": true,
- "task_hashes": true,
- "n-samples": true,
- "n-shot": true,
- "git_hash": true,
- "transformers_version": true,
- "chat_template": true,
- "chat_template_sha": true,
- "system_instruction": true,
- "fewshot_as_multiturn": true
- },
- "evaluation_config_differences": {
- "model_args": {
- "older_100k": "pretrained=slovak-nlp/mistral-sk-7b,peft=/home/schwarc/diplomovka/hruska_compare9_100k_fixed_04_rank32_a32_lr5e5,dtype=bfloat16",
- "new_300k": "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": {
- "older_100k": 7325618176,
- "new_300k": 7241732096
- },
- "model_sha": {
- "older_100k": "089497ae0d72018e9591895c54f774bf0b4a83a4",
- "new_300k": ""
- },
- "peft_sha": {
- "older_100k": "",
- "new_300k": null
- }
- }
- },
- {
- "model_id": "05_r16_alpha32_lr5e5",
- "equal_to_300k": {
- "versions": true,
- "configs": true,
- "task_hashes": true,
- "n-samples": true,
- "n-shot": true,
- "git_hash": true,
- "transformers_version": true,
- "chat_template": true,
- "chat_template_sha": true,
- "system_instruction": true,
- "fewshot_as_multiturn": true
- },
- "evaluation_config_differences": {
- "model_args": {
- "older_100k": "pretrained=slovak-nlp/mistral-sk-7b,peft=/home/schwarc/diplomovka/hruska_compare9_100k_fixed_05_r16_alpha32_lr5e5,dtype=bfloat16",
- "new_300k": "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": {
- "older_100k": 7283675136,
- "new_300k": 7241732096
- },
- "model_sha": {
- "older_100k": "089497ae0d72018e9591895c54f774bf0b4a83a4",
- "new_300k": ""
- },
- "peft_sha": {
- "older_100k": "",
- "new_300k": null
- }
- }
- },
- {
- "model_id": "06_lr1e4_r16_a16",
- "equal_to_300k": {
- "versions": true,
- "configs": true,
- "task_hashes": true,
- "n-samples": true,
- "n-shot": true,
- "git_hash": true,
- "transformers_version": true,
- "chat_template": true,
- "chat_template_sha": true,
- "system_instruction": true,
- "fewshot_as_multiturn": true
- },
- "evaluation_config_differences": {
- "model_args": {
- "older_100k": "pretrained=slovak-nlp/mistral-sk-7b,peft=/home/schwarc/diplomovka/hruska_compare9_100k_fixed_06_lr1e4_r16_a16,dtype=bfloat16",
- "new_300k": "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": {
- "older_100k": 7283675136,
- "new_300k": 7241732096
- },
- "model_sha": {
- "older_100k": "089497ae0d72018e9591895c54f774bf0b4a83a4",
- "new_300k": ""
- },
- "peft_sha": {
- "older_100k": "",
- "new_300k": null
- }
- }
- },
- {
- "model_id": "07_lr2e5_r16_a16",
- "equal_to_300k": {
- "versions": true,
- "configs": true,
- "task_hashes": true,
- "n-samples": true,
- "n-shot": true,
- "git_hash": true,
- "transformers_version": true,
- "chat_template": true,
- "chat_template_sha": true,
- "system_instruction": true,
- "fewshot_as_multiturn": true
- },
- "evaluation_config_differences": {
- "model_args": {
- "older_100k": "pretrained=slovak-nlp/mistral-sk-7b,peft=/home/schwarc/diplomovka/hruska_compare9_100k_fixed_07_lr2e5_r16_a16,dtype=bfloat16",
- "new_300k": "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": {
- "older_100k": 7283675136,
- "new_300k": 7241732096
- },
- "model_sha": {
- "older_100k": "089497ae0d72018e9591895c54f774bf0b4a83a4",
- "new_300k": ""
- },
- "peft_sha": {
- "older_100k": "",
- "new_300k": null
- }
- }
- },
- {
- "model_id": "08_attention_only_r16_a16_lr5e5",
- "equal_to_300k": {
- "versions": true,
- "configs": true,
- "task_hashes": true,
- "n-samples": true,
- "n-shot": true,
- "git_hash": true,
- "transformers_version": true,
- "chat_template": true,
- "chat_template_sha": true,
- "system_instruction": true,
- "fewshot_as_multiturn": true
- },
- "evaluation_config_differences": {
- "model_args": {
- "older_100k": "pretrained=slovak-nlp/mistral-sk-7b,peft=/home/schwarc/diplomovka/hruska_compare9_100k_fixed_08_attention_only_r16_a16_lr5e5,dtype=bfloat16",
- "new_300k": "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": {
- "older_100k": 7255363584,
- "new_300k": 7241732096
- },
- "model_sha": {
- "older_100k": "089497ae0d72018e9591895c54f774bf0b4a83a4",
- "new_300k": ""
- },
- "peft_sha": {
- "older_100k": "",
- "new_300k": null
- }
- }
- },
- {
- "model_id": "09_batch2_accum4_r16_a16_lr5e5",
- "equal_to_300k": {
- "versions": true,
- "configs": true,
- "task_hashes": true,
- "n-samples": true,
- "n-shot": true,
- "git_hash": true,
- "transformers_version": true,
- "chat_template": true,
- "chat_template_sha": true,
- "system_instruction": true,
- "fewshot_as_multiturn": true
- },
- "evaluation_config_differences": {
- "model_args": {
- "older_100k": "pretrained=slovak-nlp/mistral-sk-7b,peft=/home/schwarc/diplomovka/hruska_compare9_100k_fixed_09_batch2_accum4_r16_a16_lr5e5,dtype=bfloat16",
- "new_300k": "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": {
- "older_100k": 7283675136,
- "new_300k": 7241732096
- },
- "model_sha": {
- "older_100k": "089497ae0d72018e9591895c54f774bf0b4a83a4",
- "new_300k": ""
- },
- "peft_sha": {
- "older_100k": "",
- "new_300k": null
- }
- }
- },
- {
- "model_id": "10_r16_a32_lr1e4",
- "equal_to_300k": {
- "versions": true,
- "configs": true,
- "task_hashes": true,
- "n-samples": true,
- "n-shot": true,
- "git_hash": true,
- "transformers_version": true,
- "chat_template": true,
- "chat_template_sha": true,
- "system_instruction": true,
- "fewshot_as_multiturn": true
- },
- "evaluation_config_differences": {
- "model_args": {
- "older_100k": "pretrained=slovak-nlp/mistral-sk-7b,peft=/home/schwarc/diplomovka/hruska_compare9_100k_fixed_10_r16_a32_lr1e4,dtype=bfloat16",
- "new_300k": "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": {
- "older_100k": 7283675136,
- "new_300k": 7241732096
- },
- "model_sha": {
- "older_100k": "089497ae0d72018e9591895c54f774bf0b4a83a4",
- "new_300k": ""
- },
- "peft_sha": {
- "older_100k": "",
- "new_300k": null
- }
- }
- }
- ]
-}
diff --git a/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/arc_sk.yaml b/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/arc_sk.yaml
deleted file mode 100644
index 2cb9894..0000000
--- a/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/arc_sk.yaml
+++ /dev/null
@@ -1,42 +0,0 @@
-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: '
-
-
- '
-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
deleted file mode 100644
index 2c96e72..0000000
--- a/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/hellaswag_sk.yaml
+++ /dev/null
@@ -1,35 +0,0 @@
-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
deleted file mode 100644
index 501237a..0000000
--- a/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/m_mmlu_sk.yaml
+++ /dev/null
@@ -1,43 +0,0 @@
-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
deleted file mode 100644
index 814685f..0000000
--- a/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/sklegal.yaml
+++ /dev/null
@@ -1,23 +0,0 @@
-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
deleted file mode 100644
index 2546a99..0000000
--- a/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/skquad.yaml
+++ /dev/null
@@ -1,2 +0,0 @@
-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
deleted file mode 100644
index c9e55f7..0000000
--- a/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/task.py
+++ /dev/null
@@ -1,245 +0,0 @@
-"""
-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
deleted file mode 100644
index 99a3cb3..0000000
--- a/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/task_configs_from_wandb.json
+++ /dev/null
@@ -1,232 +0,0 @@
-{
- "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
deleted file mode 100644
index 92b1a45..0000000
--- a/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/truthfulqa_sk_mc1.yaml
+++ /dev/null
@@ -1,34 +0,0 @@
-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
deleted file mode 100644
index 6684fa7..0000000
--- a/aya_300k_lr_experiments/benchmarks/lmeval/provenance/custom_tasks/truthfulqa_sk_mc2.yaml
+++ /dev/null
@@ -1,40 +0,0 @@
-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
deleted file mode 100644
index af55d49..0000000
--- a/aya_300k_lr_experiments/benchmarks/lmeval/provenance/lm_eval_commit.txt
+++ /dev/null
@@ -1 +0,0 @@
-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
deleted file mode 100644
index f351a5e..0000000
--- a/aya_300k_lr_experiments/benchmarks/lmeval/provenance/package_versions.json
+++ /dev/null
@@ -1,8 +0,0 @@
-{
- "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
deleted file mode 100644
index 47fe7cf..0000000
--- a/aya_300k_lr_experiments/benchmarks/lmeval/provenance/run_settings.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-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
deleted file mode 100644
index 2ce65ea..0000000
--- a/aya_300k_lr_experiments/benchmarks/lmeval/provenance/tasks.txt
+++ /dev/null
@@ -1 +0,0 @@
-arc_sk,hellaswag_sk,m_mmlu_sk,truthfulqa_sk_mc1,truthfulqa_sk_mc2,sklegal,skquad
diff --git a/aya_300k_lr_experiments/benchmarks/skbench/audit.md b/aya_300k_lr_experiments/benchmarks/skbench/audit.md
deleted file mode 100644
index 4e3b0e9..0000000
--- a/aya_300k_lr_experiments/benchmarks/skbench/audit.md
+++ /dev/null
@@ -1,82 +0,0 @@
-# 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/provenance/archive_summary.json b/aya_300k_lr_experiments/benchmarks/skbench/provenance/archive_summary.json
deleted file mode 100644
index 6f0477a..0000000
--- a/aya_300k_lr_experiments/benchmarks/skbench/provenance/archive_summary.json
+++ /dev/null
@@ -1,87 +0,0 @@
-{
- "source": "C:\\Users\\Jakub\\Desktop\\aya_300k_benchmarky_20261024_215708.tar.gz",
- "compressed_bytes": 349555561,
- "sha256": "b45e431cbc63bc73ef953f8aa3837695ccec43783e3da2dfccb0d99f7d1fe79c",
- "files": 581,
- "uncompressed_bytes": 1782181826,
- "skipped_special": [],
- "top_level_files": {
- "aya_300k_lr_validation": 110,
- "skbench_evaluation": 464,
- "scripts": 7
- },
- "largest_files": [
- {
- "original": "skbench_evaluation/results/sk_zero_available_without_nli_lr_0p00010_20261022_185255/details/home/schwarc/diplomovka/skbench_evaluation/models/aya_300k_lr_0p00010/2026-10-22T21-55-04.268099/details_multi_wiki_qa.prompt2_qa_sk|0_2026-10-22T21-55-04.268099.parquet",
- "local": "skbench_evaluation/results/sk_zero_available_without_nli_lr_0p00010_20261022_185255/details/home/schwarc/diplomovka/skbench_evaluation/models/aya_300k_lr_0p00010/2026-10-22T21-55-04.268099/details_multi_wiki_qa.prompt2_qa_sk%7C0_2026-10-22T21-55-04.268099.parquet",
- "size": 127542209,
- "type": "file"
- },
- {
- "original": "skbench_evaluation/results/sk_zero_available_without_nli_lr_0p00010_20261022_185255/details/home/schwarc/diplomovka/skbench_evaluation/models/aya_300k_lr_0p00010/2026-10-22T21-55-04.268099/details_multi_wiki_qa.prompt1_qa_sk|0_2026-10-22T21-55-04.268099.parquet",
- "local": "skbench_evaluation/results/sk_zero_available_without_nli_lr_0p00010_20261022_185255/details/home/schwarc/diplomovka/skbench_evaluation/models/aya_300k_lr_0p00010/2026-10-22T21-55-04.268099/details_multi_wiki_qa.prompt1_qa_sk%7C0_2026-10-22T21-55-04.268099.parquet",
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- "local": "skbench_evaluation/results/sk_zero_available_without_nli_lr_0p00010_20261022_185255/details/home/schwarc/diplomovka/skbench_evaluation/models/aya_300k_lr_0p00010/2026-10-22T21-55-04.268099/details_multi_wiki_qa.prompt3_qa_sk%7C0_2026-10-22T21-55-04.268099.parquet",
- "size": 125437876,
- "type": "file"
- },
- {
- "original": "skbench_evaluation/models/aya_300k_lr_0p00010/d0e7048bfa3b2274/multi_wiki_qa.prompt2_qa_sk|0/82e1a28fce479a47/GENERATIVE.parquet",
- "local": "skbench_evaluation/models/aya_300k_lr_0p00010/d0e7048bfa3b2274/multi_wiki_qa.prompt2_qa_sk%7C0/82e1a28fce479a47/GENERATIVE.parquet",
- "size": 103508951,
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- },
- {
- "original": "skbench_evaluation/models/aya_300k_lr_0p00010/d0e7048bfa3b2274/multi_wiki_qa.prompt1_qa_sk|0/f0737eb9e82c262f/GENERATIVE.parquet",
- "local": "skbench_evaluation/models/aya_300k_lr_0p00010/d0e7048bfa3b2274/multi_wiki_qa.prompt1_qa_sk%7C0/f0737eb9e82c262f/GENERATIVE.parquet",
- "size": 103246110,
- "type": "file"
- },
- {
- "original": "skbench_evaluation/models/aya_300k_lr_0p00010/d0e7048bfa3b2274/multi_wiki_qa.prompt3_qa_sk|0/579d61d41168b7f2/GENERATIVE.parquet",
- "local": "skbench_evaluation/models/aya_300k_lr_0p00010/d0e7048bfa3b2274/multi_wiki_qa.prompt3_qa_sk%7C0/579d61d41168b7f2/GENERATIVE.parquet",
- "size": 101814277,
- "type": "file"
- },
- {
- "original": "skbench_evaluation/results/sk_zero_available_without_nli_lr_0p00010_20261022_185255/details/home/schwarc/diplomovka/skbench_evaluation/models/aya_300k_lr_0p00010/2026-10-22T21-55-04.268099/details_sklep.prompt2_qa_sk:question-answering|0_2026-10-22T21-55-04.268099.parquet",
- "local": "skbench_evaluation/results/sk_zero_available_without_nli_lr_0p00010_20261022_185255/details/home/schwarc/diplomovka/skbench_evaluation/models/aya_300k_lr_0p00010/2026-10-22T21-55-04.268099/details_sklep.prompt2_qa_sk%3Aquestion-answering%7C0_2026-10-22T21-55-04.268099.parquet",
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- "type": "file"
- },
- {
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- "local": "skbench_evaluation/results/sk_zero_available_without_nli_lr_0p00010_20261022_185255/details/home/schwarc/diplomovka/skbench_evaluation/models/aya_300k_lr_0p00010/2026-10-22T21-55-04.268099/details_sklep.prompt1_qa_sk%3Aquestion-answering%7C0_2026-10-22T21-55-04.268099.parquet",
- "size": 66012897,
- "type": "file"
- },
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- "local": "skbench_evaluation/results/sk_zero_available_without_nli_lr_0p00010_20261022_185255/details/home/schwarc/diplomovka/skbench_evaluation/models/aya_300k_lr_0p00010/2026-10-22T21-55-04.268099/details_sklep.prompt3_qa_sk%3Aquestion-answering%7C0_2026-10-22T21-55-04.268099.parquet",
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- "local": "skbench_evaluation/models/aya_300k_lr_0p00010/d0e7048bfa3b2274/sklep.prompt2_qa_sk%3Aquestion-answering%7C0/46d75c2a99a95ed9/GENERATIVE.parquet",
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- "type": "file"
- },
- {
- "original": "skbench_evaluation/models/aya_300k_lr_0p00010/d0e7048bfa3b2274/sklep.prompt1_qa_sk:question-answering|0/b09ec46c034c8de4/GENERATIVE.parquet",
- "local": "skbench_evaluation/models/aya_300k_lr_0p00010/d0e7048bfa3b2274/sklep.prompt1_qa_sk%3Aquestion-answering%7C0/b09ec46c034c8de4/GENERATIVE.parquet",
- "size": 53089807,
- "type": "file"
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- "local": "skbench_evaluation/models/aya_300k_lr_0p00010/d0e7048bfa3b2274/sklep.prompt3_qa_sk%3Aquestion-answering%7C0/06ebedbab75fe49b/GENERATIVE.parquet",
- "size": 51637093,
- "type": "file"
- }
- ]
-}
\ No newline at end of file
diff --git a/aya_300k_lr_experiments/benchmarks/skbench/provenance/audit_skbench.py b/aya_300k_lr_experiments/benchmarks/skbench/provenance/audit_skbench.py
deleted file mode 100644
index 8cf5838..0000000
--- a/aya_300k_lr_experiments/benchmarks/skbench/provenance/audit_skbench.py
+++ /dev/null
@@ -1,234 +0,0 @@
-"""Curate the completed 300k learning-rate SK-bench run without copying raw data.
-
-Requires Python 3.10+ and pyarrow (installed separately). The only read of the
-large details parquets projects model_response.truncated_tokens_count; row counts
-come from Parquet metadata. Cached GENERATIVE.parquet files are never read.
-"""
-from __future__ import annotations
-
-import argparse
-import collections
-import csv
-import hashlib
-import json
-import pathlib
-import re
-import shutil
-import statistics
-import sys
-from urllib.parse import unquote
-
-
-def write_csv(path, rows):
- if not rows:
- return
- columns = list(dict.fromkeys(k for row in rows for k in row))
- with path.open('w', newline='', encoding='utf-8') as stream:
- writer = csv.DictWriter(stream, fieldnames=columns)
- writer.writeheader()
- writer.writerows(rows)
-
-
-def normalize(name):
- name = unquote(name.strip())
- return re.sub(r'\|([0-9]+)$', r':\1', name)
-
-
-def get_lines(path):
- return [line.strip() for line in path.read_text(encoding='utf-8').splitlines() if line.strip() and not line.startswith('#')]
-
-
-def main():
- parser = argparse.ArgumentParser(description=__doc__)
- parser.add_argument('--source', type=pathlib.Path, required=True, help='Extracted archive root containing skbench_evaluation/')
- parser.add_argument('--output', type=pathlib.Path, required=True)
- parser.add_argument('--pyarrow-path', type=pathlib.Path, help='Optional project-local pip --target directory')
- args = parser.parse_args()
- if args.pyarrow_path:
- sys.path.insert(0, str(args.pyarrow_path.resolve()))
- import pyarrow
- import pyarrow.parquet as pq
-
- source = args.source.resolve()
- out = args.output.resolve()
- base = source / 'skbench_evaluation'
- run_name = 'sk_zero_available_without_nli_lr_0p00010_20261022_185255'
- run = base / 'results' / run_name
- for name in ['results', 'logs', 'configs', 'provenance']:
- (out/name).mkdir(parents=True, exist_ok=True)
- result_path = run/'results/home/schwarc/diplomovka/skbench_evaluation/models/aya_300k_lr_0p00010/results_2026-10-22T21-55-04.268099.json'
- d = json.loads(result_path.read_text(encoding='utf-8'))
- tasks = get_lines(run/'tasks.txt')
- omitted = get_lines(run/'omitted_tasks.txt')
- configs = d['config_tasks']
- normalized_expected = {normalize(x) for x in tasks}
- assert len(tasks) == len(normalized_expected) == 222
- assert normalized_expected == {normalize(x) for x in configs}, 'Config task coverage mismatch'
- assert normalized_expected <= {normalize(x) for x in d['results']}, 'Result task coverage mismatch'
- assert len(omitted) == 190
- all_tasks = get_lines(base/'configs/tasks_sk_zero.txt')
- assert normalized_expected.isdisjoint({normalize(x) for x in omitted})
- assert normalized_expected | {normalize(x) for x in omitted} == {normalize(x) for x in all_tasks}
-
- copies = {result_path: out/'results'/result_path.name,
- base/'logs'/f'{run_name}.log': out/'logs'/f'{run_name}.log',
- run/'tasks.txt': out/'configs/tasks.txt',
- run/'omitted_tasks.txt': out/'configs/omitted_tasks.txt',
- run/'uv.lock': out/'provenance/uv.lock',
- run/'skbench_commit.txt': out/'provenance/skbench_commit.txt',
- source/'archive_summary.json': out/'provenance/archive_summary.json'}
- for path in sorted((base/'configs').iterdir()):
- if path.is_file():
- copies[path] = out/'configs'/path.name
- provenance = []
- for src, dst in copies.items():
- shutil.copyfile(src, dst)
- data = src.read_bytes()
- provenance.append({'source_archive_member': src.relative_to(source).as_posix(),
- 'curated_path': dst.relative_to(out).as_posix(),
- 'bytes': len(data), 'sha256': hashlib.sha256(data).hexdigest()})
- write_csv(out/'provenance/copied_files_sha256.csv', provenance)
- (out/'configs/model_config.json').write_text(json.dumps(d['config_general'], indent=2, ensure_ascii=False)+'\n', encoding='utf-8')
- (out/'configs/task_configs.json').write_text(json.dumps(configs, indent=2, ensure_ascii=False)+'\n', encoding='utf-8')
-
- # Resolve the original names from the extraction manifest; Windows paths
- # encode ':' and '|' as %3A and %7C, while payload bytes were unchanged.
- manifest = json.loads((source/'archive_manifest.json').read_text(encoding='utf-8'))
- if isinstance(manifest, dict):
- manifest = manifest.get('entries', manifest.get('files', []))
- original_paths = {x['local']: x['original'] for x in manifest if isinstance(x, dict) and 'local' in x}
- details = []
- lookup = {}
- files = sorted((run/'details').rglob('*.parquet'))
- assert len(files) == 222, f'Expected 222 DETAILS files, got {len(files)}'
- for file in files:
- decoded = unquote(file.name)
- task = re.match(r'^details_(.+)_2026-10-22T21-55-04\.268099\.parquet$', decoded).group(1)
- pf = pq.ParquetFile(file)
- projected = pf.read(columns=['model_response.truncated_tokens_count'])
- values = projected.column('model_response').combine_chunks().field('truncated_tokens_count').to_pylist()
- assert len(values) == pf.metadata.num_rows
- assert all(v is None or (isinstance(v, int) and v >= 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
deleted file mode 100644
index 8a6bbd7..0000000
--- a/aya_300k_lr_experiments/benchmarks/skbench/provenance/copied_files_sha256.csv
+++ /dev/null
@@ -1,14 +0,0 @@
-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
-skbench_evaluation/results/sk_zero_available_without_nli_lr_0p00010_20261022_185255/tasks.txt,configs/tasks.txt,11415,297c7185d8ddea6899d20287ed5cffb895116cf756121252a0e76070b248db76
-skbench_evaluation/results/sk_zero_available_without_nli_lr_0p00010_20261022_185255/omitted_tasks.txt,configs/omitted_tasks.txt,8629,613b78472e6788eab826f31da1100c00b597ed9bbc8151af38ebb8ef5bf830af
-skbench_evaluation/results/sk_zero_available_without_nli_lr_0p00010_20261022_185255/uv.lock,provenance/uv.lock,690556,045d2840fda141981afba93bd4a1b9ddb59f0f0791c162cf66b2c88e6491fb41
-skbench_evaluation/results/sk_zero_available_without_nli_lr_0p00010_20261022_185255/skbench_commit.txt,provenance/skbench_commit.txt,41,12655509d516c6a57225a072f708e5ca854600e7daa3bf5cdc95e6b36e94fe70
-archive_summary.json,provenance/archive_summary.json,6529,535390044cd67477ea1631527ac736f72108935f25a11a613b1709caf8154957
-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
deleted file mode 100644
index b95f6eb..0000000
--- a/aya_300k_lr_experiments/benchmarks/skbench/provenance/input_length_diagnostics.py
+++ /dev/null
@@ -1,62 +0,0 @@
-"""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
deleted file mode 100644
index 8ed4982..0000000
--- a/aya_300k_lr_experiments/benchmarks/skbench/provenance/requirements-audit.txt
+++ /dev/null
@@ -1 +0,0 @@
-pyarrow==26.0.0
diff --git a/aya_300k_lr_experiments/benchmarks/skbench/provenance/skbench_commit.txt b/aya_300k_lr_experiments/benchmarks/skbench/provenance/skbench_commit.txt
deleted file mode 100644
index 0cc84fb..0000000
--- a/aya_300k_lr_experiments/benchmarks/skbench/provenance/skbench_commit.txt
+++ /dev/null
@@ -1 +0,0 @@
-15823bbcc2d995e69441fcb7d34abb61bd8180c5
diff --git a/aya_300k_lr_experiments/benchmarks/skbench/provenance/uv.lock b/aya_300k_lr_experiments/benchmarks/skbench/provenance/uv.lock
deleted file mode 100644
index a734062..0000000
--- a/aya_300k_lr_experiments/benchmarks/skbench/provenance/uv.lock
+++ /dev/null
@@ -1,6274 +0,0 @@
-version = 1
-revision = 3
-requires-python = "==3.13.*"
-resolution-markers = [
- "sys_platform == 'win32'",
- "sys_platform == 'emscripten'",
- "sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'",
- "sys_platform == 'darwin'",
-]
-
-[manifest]
-
-[[manifest.dependency-metadata]]
-name = "deep-gemm"
-version = "2.1.1.post3"
-
-[[package]]
-name = "absl-py"
-version = "2.4.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/64/c7/8de93764ad66968d19329a7e0c147a2bb3c7054c554d4a119111b8f9440f/absl_py-2.4.0.tar.gz", hash = "sha256:8c6af82722b35cf71e0f4d1d47dcaebfff286e27110a99fc359349b247dfb5d4", size = 116543, upload-time = "2026-01-28T10:17:05.322Z" }
-wheels = [
- { url = "https://files.pythonhosted.org/packages/18/a6/907a406bb7d359e6a63f99c313846d9eec4f7e6f7437809e03aa00fa3074/absl_py-2.4.0-py3-none-any.whl", hash = "sha256:88476fd881ca8aab94ffa78b7b6c632a782ab3ba1cd19c9bd423abc4fb4cd28d", size = 135750, upload-time = "2026-01-28T10:17:04.19Z" },
-]
-
-[[package]]
-name = "accelerate"
-version = "1.14.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
- { name = "huggingface-hub" },
- { name = "numpy" },
- { name = "packaging" },
- { name = "psutil" },
- { name = "pyyaml" },
- { name = "safetensors" },
- { name = "torch" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/8d/75/94cd5d389649578aca399e5aa822637eec18319a1dadc400ffe2f9a7493f/accelerate-1.14.0.tar.gz", hash = "sha256:41b9c4377a54e0b460a959b0defa1b736e4ca0a2373252d9a539964c2afe3c8d", size = 412167, upload-time = "2026-06-11T13:45:52.326Z" }
-wheels = [
- { url = "https://files.pythonhosted.org/packages/a8/db/253133d7e7cb40d3af384bb2f5c0b4a2b7fdcffbc95c688cc67a20a3c103/accelerate-1.14.0-py3-none-any.whl", hash = "sha256:e94390c2863b873be18f623f9df48a0d8fe5eff13ea7f1a00092b0a7904888c6", size = 389246, upload-time = "2026-06-11T13:45:50.477Z" },
-]
-
-[[package]]
-name = "aenum"
-version = "3.1.15"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/d0/f8/33e75863394f42e429bb553e05fda7c59763f0fd6848de847a25b3fbccf6/aenum-3.1.15.tar.gz", hash = "sha256:8cbd76cd18c4f870ff39b24284d3ea028fbe8731a58df3aa581e434c575b9559", size = 134730, upload-time = "2023-06-27T00:19:52.546Z" }
-wheels = [
- { url = "https://files.pythonhosted.org/packages/d0/fa/ca0c66b388624ba9dbbf35aab3a9f326bfdf5e56a7237fe8f1b600da6864/aenum-3.1.15-py3-none-any.whl", hash = "sha256:e0dfaeea4c2bd362144b87377e2c61d91958c5ed0b4daf89cb6f45ae23af6288", size = 137633, upload-time = "2023-06-27T00:19:55.112Z" },
-]
-
-[[package]]
-name = "aioboto3"
-version = "15.5.0"
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diff --git a/aya_300k_lr_experiments/provenance/source_archive.json b/aya_300k_lr_experiments/provenance/source_archive.json
deleted file mode 100644
index 964c2b9..0000000
--- a/aya_300k_lr_experiments/provenance/source_archive.json
+++ /dev/null
@@ -1,12 +0,0 @@
-{
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- "sha256": "b45e431cbc63bc73ef953f8aa3837695ccec43783e3da2dfccb0d99f7d1fe79c",
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- "scope": "Hruska Aya 300k learning-rate selection and evaluation of selected LR 0.00010; Titan excluded",
- "base_model": "slovak-nlp/mistral-sk-7b",
- "base_model_revision": "089497ae0d72018e9591895c54f774bf0b4a83a4",
- "selection_criterion": "lowest final validation loss among the six completed 300k runs",
- "intended_next_stage": "planned training on the full Aya dataset on Perun; not executed in this experiment",
- "data_reproducibility_limit": "No source dataset revision, data-file checksum, or held-out sample IDs were included in this archive",
- "curation": "Model weights, checkpoints, trainer_state.json, duplicated inference caches and full parquet details excluded from the Git subset. Original archive retained separately."
-}
diff --git a/aya_300k_lr_experiments/results/eval_loss_history.csv b/aya_300k_lr_experiments/results/eval_loss_history.csv
deleted file mode 100644
index f9ead6d..0000000
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diff --git a/aya_300k_lr_experiments/results/gradient_history.csv b/aya_300k_lr_experiments/results/gradient_history.csv
deleted file mode 100644
index c0de271..0000000
--- a/aya_300k_lr_experiments/results/gradient_history.csv
+++ /dev/null
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-0.00023,lr_0p00023,17700,1.5578513145446777
-0.00023,lr_0p00023,17710,1.6570184230804443
-0.00023,lr_0p00023,17720,1.9629621505737305
-0.00023,lr_0p00023,17730,1.5204099416732788
-0.00023,lr_0p00023,17740,1.6048884391784668
-0.00023,lr_0p00023,17750,1.7357628345489502
-0.00023,lr_0p00023,17760,1.669527530670166
-0.00023,lr_0p00023,17770,2.226815700531006
-0.00023,lr_0p00023,17780,1.9395203590393066
-0.00023,lr_0p00023,17790,2.044400691986084
-0.00023,lr_0p00023,17800,1.935316562652588
-0.00023,lr_0p00023,17810,1.621355414390564
-0.00023,lr_0p00023,17820,1.6787899732589722
-0.00023,lr_0p00023,17830,1.7389189004898071
-0.00023,lr_0p00023,17840,1.7355002164840698
-0.00023,lr_0p00023,17850,1.5743194818496704
-0.00023,lr_0p00023,17860,1.8340767621994019
-0.00023,lr_0p00023,17870,1.7096129655838013
-0.00023,lr_0p00023,17880,1.5048872232437134
-0.00023,lr_0p00023,17890,1.8583996295928955
-0.00023,lr_0p00023,17900,1.6688668727874756
-0.00023,lr_0p00023,17910,1.8556867837905884
-0.00023,lr_0p00023,17920,1.6653673648834229
-0.00023,lr_0p00023,17930,1.8894134759902954
-0.00023,lr_0p00023,17940,1.5242680311203003
-0.00023,lr_0p00023,17950,1.7481228113174438
-0.00023,lr_0p00023,17960,2.0547523498535156
-0.00023,lr_0p00023,17970,2.0163118839263916
-0.00023,lr_0p00023,17980,2.1662518978118896
-0.00023,lr_0p00023,17990,2.0338683128356934
-0.00023,lr_0p00023,18000,1.8623000383377075
-0.00023,lr_0p00023,18010,1.954721212387085
-0.00023,lr_0p00023,18020,1.625108003616333
-0.00023,lr_0p00023,18030,1.6790188550949097
-0.00023,lr_0p00023,18040,1.754738211631775
-0.00023,lr_0p00023,18050,2.0219826698303223
-0.00023,lr_0p00023,18060,2.163019895553589
-0.00023,lr_0p00023,18070,1.918573260307312
-0.00023,lr_0p00023,18080,1.6322234869003296
-0.00023,lr_0p00023,18090,1.8298832178115845
-0.00023,lr_0p00023,18100,1.2878504991531372
-0.00023,lr_0p00023,18110,1.2585750818252563
-0.00023,lr_0p00023,18120,1.5566447973251343
-0.00023,lr_0p00023,18130,1.8851546049118042
-0.00023,lr_0p00023,18140,1.4553338289260864
-0.00023,lr_0p00023,18150,2.337697982788086
-0.00023,lr_0p00023,18160,1.648917555809021
-0.00023,lr_0p00023,18170,1.895189881324768
-0.00023,lr_0p00023,18180,1.7663055658340454
-0.00023,lr_0p00023,18190,2.031618118286133
-0.00023,lr_0p00023,18200,1.790711522102356
-0.00023,lr_0p00023,18210,1.789526343345642
-0.00023,lr_0p00023,18220,1.5938247442245483
-0.00023,lr_0p00023,18230,1.73521888256073
-0.00023,lr_0p00023,18240,1.7108190059661865
-0.00023,lr_0p00023,18250,1.5391435623168945
-0.00023,lr_0p00023,18260,1.7715888023376465
-0.00023,lr_0p00023,18270,1.714411973953247
-0.00023,lr_0p00023,18280,1.6153943538665771
-0.00023,lr_0p00023,18290,1.9462387561798096
-0.00023,lr_0p00023,18300,1.6351021528244019
-0.00023,lr_0p00023,18310,1.8084819316864014
-0.00023,lr_0p00023,18320,1.681557059288025
-0.00023,lr_0p00023,18330,1.5786561965942383
-0.00023,lr_0p00023,18340,1.774751901626587
-0.00023,lr_0p00023,18350,1.5220043659210205
-0.00023,lr_0p00023,18360,1.5686100721359253
-0.00023,lr_0p00023,18370,1.7094277143478394
-0.00023,lr_0p00023,18380,1.7287569046020508
-0.00023,lr_0p00023,18390,1.5621235370635986
-0.00023,lr_0p00023,18400,1.8235636949539185
-0.00023,lr_0p00023,18410,1.7235445976257324
-0.00023,lr_0p00023,18420,1.7393262386322021
-0.00023,lr_0p00023,18430,2.0028252601623535
-0.00023,lr_0p00023,18440,1.5244539976119995
-0.00023,lr_0p00023,18450,1.8141977787017822
-0.00023,lr_0p00023,18460,1.983327865600586
-0.00023,lr_0p00023,18470,1.8449904918670654
-0.00023,lr_0p00023,18480,1.48514723777771
-0.00023,lr_0p00023,18490,1.2782723903656006
-0.00023,lr_0p00023,18500,1.4412953853607178
-0.00023,lr_0p00023,18510,1.8712087869644165
-0.00023,lr_0p00023,18520,1.6926734447479248
-0.00023,lr_0p00023,18530,1.5156067609786987
-0.00023,lr_0p00023,18540,1.4491909742355347
-0.00023,lr_0p00023,18550,1.3928817510604858
-0.00023,lr_0p00023,18560,1.5048515796661377
-0.00023,lr_0p00023,18570,1.733676791191101
-0.00023,lr_0p00023,18580,2.074488639831543
-0.00023,lr_0p00023,18590,1.99159836769104
-0.00023,lr_0p00023,18600,1.7074240446090698
-0.00023,lr_0p00023,18610,1.4553114175796509
-0.00023,lr_0p00023,18620,1.5315895080566406
-0.00023,lr_0p00023,18630,1.2433881759643555
-0.00023,lr_0p00023,18640,1.6193467378616333
-0.00023,lr_0p00023,18650,1.6077945232391357
-0.00023,lr_0p00023,18660,1.7057081460952759
-0.00023,lr_0p00023,18670,1.6365538835525513
-0.00023,lr_0p00023,18680,1.5130199193954468
diff --git a/aya_300k_lr_experiments/results/lr_vs_final_eval_loss.png b/aya_300k_lr_experiments/results/lr_vs_final_eval_loss.png
deleted file mode 100644
index e1c77a1..0000000
Binary files a/aya_300k_lr_experiments/results/lr_vs_final_eval_loss.png and /dev/null differ
diff --git a/aya_300k_lr_experiments/scripts/analyze_aya_300k_lr.py b/aya_300k_lr_experiments/scripts/analyze_aya_300k_lr.py
deleted file mode 100644
index ad122ef..0000000
--- a/aya_300k_lr_experiments/scripts/analyze_aya_300k_lr.py
+++ /dev/null
@@ -1,338 +0,0 @@
-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
deleted file mode 100644
index c334300..0000000
--- a/aya_300k_lr_experiments/scripts/analyze_aya_300k_lr_all.py
+++ /dev/null
@@ -1,341 +0,0 @@
-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
deleted file mode 100644
index ea8ad64..0000000
--- a/aya_300k_lr_experiments/scripts/analyze_training_results.py
+++ /dev/null
@@ -1,119 +0,0 @@
-#!/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
deleted file mode 100644
index a0dc63d..0000000
--- a/aya_300k_lr_experiments/scripts/rerun_skbench_lr_0p00010.sh
+++ /dev/null
@@ -1,37 +0,0 @@
-#!/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 "$RUN/run.log"
diff --git a/aya_300k_lr_experiments/scripts/run_aya_300k_lr_validation.sh b/aya_300k_lr_experiments/scripts/run_aya_300k_lr_validation.sh
deleted file mode 100644
index 1c4efde..0000000
--- a/aya_300k_lr_experiments/scripts/run_aya_300k_lr_validation.sh
+++ /dev/null
@@ -1,76 +0,0 @@
-#!/usr/bin/env bash
-set -euo pipefail
-
-source /home/schwarc/miniconda3/etc/profile.d/conda.sh
-conda activate llama_factory_unsloth
-
-ROOT="/home/schwarc/diplomovka"
-EXP="$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 \
- "$ROOT/scripts/analyze_aya_300k_lr.py"
diff --git a/aya_300k_lr_experiments/scripts/run_aya_300k_lr_validation_lower.sh b/aya_300k_lr_experiments/scripts/run_aya_300k_lr_validation_lower.sh
deleted file mode 100644
index 5289159..0000000
--- a/aya_300k_lr_experiments/scripts/run_aya_300k_lr_validation_lower.sh
+++ /dev/null
@@ -1,76 +0,0 @@
-#!/usr/bin/env bash
-set -euo pipefail
-
-source /home/schwarc/miniconda3/etc/profile.d/conda.sh
-conda activate llama_factory_unsloth
-
-ROOT="/home/schwarc/diplomovka"
-EXP="$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 \
- "$ROOT/scripts/analyze_aya_300k_lr_all.py"
diff --git a/aya_300k_lr_experiments/scripts/run_lmeval_aya_300k_lr_0p00010.sh b/aya_300k_lr_experiments/scripts/run_lmeval_aya_300k_lr_0p00010.sh
deleted file mode 100644
index 6ca0bcb..0000000
--- a/aya_300k_lr_experiments/scripts/run_lmeval_aya_300k_lr_0p00010.sh
+++ /dev/null
@@ -1,74 +0,0 @@
-#!/usr/bin/env bash
-set -euo pipefail
-
-eval "$(/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
deleted file mode 100644
index d8eb396..0000000
--- a/aya_300k_lr_experiments/scripts/setup_aya_300k_lr_validation.sh
+++ /dev/null
@@ -1,899 +0,0 @@
-#!/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
deleted file mode 100644
index 406c856..0000000
--- a/aya_300k_lr_experiments/scripts/unpack_results_archive.py
+++ /dev/null
@@ -1,74 +0,0 @@
-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))