add testing models files
This commit is contained in:
oleh
parent
4e0499ff05
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80
Backend/index-server-es.py
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80
Backend/index-server-es.py
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from elasticsearch import Elasticsearch
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from langchain.embeddings import HuggingFaceEmbeddings
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from elasticsearch.helpers import bulk
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import json
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# Настройка подключения к Elasticsearch с аутентификацией и HTTPS
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es = Elasticsearch(
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[{'host': 'localhost', 'port': 9200, 'scheme': 'https'}],
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http_auth=('elastic', 'S7DoO3ma=G=9USBPbqq3'), # замените на ваш пароль
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verify_certs=False # Отключить проверку SSL-сертификата, если используется самоподписанный сертификат
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)
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embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
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def create_index():
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# Определяем маппинг для индекса
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mapping = {
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"mappings": {
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"properties": {
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"text": {
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"type": "text",
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"analyzer": "standard"
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},
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"vector": {
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"type": "dense_vector",
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"dims": 384 # Размерность векторного представления
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},
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"full_data": {
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"type": "object",
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"enabled": False # Отключаем индексацию вложенных данных
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}
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}
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}
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}
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es.indices.create(index='drug_docs', body=mapping, ignore=400)
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def load_drug_data(json_path):
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with open(json_path, 'r', encoding='utf-8') as f:
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data = json.load(f)
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return data
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def index_documents(data):
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actions = []
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total_docs = len(data)
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for i, item in enumerate(data, start=1):
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doc_text = f"{item['link']} {item.get('pribalovy_letak', '')} {item.get('spc', '')}"
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vector = embeddings.embed_query(doc_text)
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action = {
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"_index": "drug_docs",
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"_id": i,
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"_source": {
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'text': doc_text,
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'vector': vector,
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'full_data': item
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}
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}
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actions.append(action)
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# Отображение прогресса
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print(f"Индексируется документ {i}/{total_docs}", end='\r')
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# Опционально: индексируем пакетами по N документов
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if i % 100 == 0 or i == total_docs:
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bulk(es, actions)
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actions = []
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# Если остались неиндексированные документы
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if actions:
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bulk(es, actions)
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print("\nИндексирование завершено.")
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if __name__ == "__main__":
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create_index()
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data_path = "/home/poiasnik/esDB/cleaned_general_info_additional.json"
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drug_data = load_drug_data(data_path)
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index_documents(drug_data)
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77
Backend/qwen72-test.py
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77
Backend/qwen72-test.py
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import torch
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import logging
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from elasticsearch import Elasticsearch
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# Настройка логирования
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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# Подключение к Elasticsearch
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es = Elasticsearch(
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["https://localhost:9200"],
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basic_auth=("elastic", "S7DoO3ma=G=9USBPbqq3"), # Ваш пароль
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verify_certs=False
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)
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index_name = 'drug_docs'
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# Загрузка токенизатора и модели
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model_name = "Qwen/Qwen2.5-7B-Instruct"
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device = "cuda:0" if torch.cuda.is_available() else "cpu"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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# Проверка наличия pad_token
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if tokenizer.pad_token is None:
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tokenizer.pad_token = tokenizer.eos_token
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def text_search(query, k=10, max_doc_length=300, max_docs=3):
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try:
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es_results = es.search(
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index=index_name,
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body={"size": k, "query": {"match": {"text": query}}}
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)
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text_documents = [hit['_source'].get('text', '') for hit in es_results['hits']['hits']]
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text_documents = [doc[:max_doc_length] for doc in text_documents[:max_docs]]
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return text_documents
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except Exception as e:
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logger.error(f"Ошибка поиска: {str(e)}")
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return []
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# Пример запроса для поиска
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query = "čo piť pri horúčke"
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text_documents = text_search(query)
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# Обрезаем текст, если он превышает предел токенов модели
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max_tokens_per_input = 1024 # Установим более низкое значение для max_tokens
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context_text = ' '.join(text_documents)
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input_text = (
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f"Informácie o liekoch: {context_text[:max_tokens_per_input]}\n"
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"Uveďte tri konkrétne lieky alebo riešenia s veľmi krátkym vysvetlením pre každý z nich.\n"
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"Odpoveď v slovenčine:"
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)
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# Токенизация входного текста
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inputs = tokenizer(input_text, return_tensors="pt", max_length=max_tokens_per_input, truncation=True).to(device)
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try:
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generated_ids = model.generate(
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inputs.input_ids,
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attention_mask=inputs.attention_mask,
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max_new_tokens=300, # Снижено значение
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temperature=0.7,
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top_k=50,
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top_p=0.9,
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do_sample=False, # Отключено семплирование для детерминированного вывода
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pad_token_id=tokenizer.pad_token_id
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)
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response = tokenizer.decode(generated_ids[0], skip_special_tokens=True, errors='ignore')
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print("Сгенерированный текст:", response)
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except RuntimeError as e:
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print(f"Произошла ошибка во время генерации: {e}")
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308
Backend/qwen7b-test.py
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308
Backend/qwen7b-test.py
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"""A simple command-line interactive chat demo for Qwen2.5-Instruct model with left-padding using bos_token."""
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import argparse
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import os
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import platform
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import shutil
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from copy import deepcopy
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from threading import Thread
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
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from transformers.trainer_utils import set_seed
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DEFAULT_CKPT_PATH = "Qwen/Qwen2.5-7B-Instruct"
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_WELCOME_MSG = """\
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Welcome to use Qwen2.5-Instruct model, type text to start chat, type :h to show command help.
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"""
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_HELP_MSG = """\
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Commands:
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:help / :h Show this help message
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:exit / :quit / :q Exit the demo
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:clear / :cl Clear screen
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:clear-history / :clh Clear history
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:history / :his Show history
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:seed Show current random seed
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:seed <N> Set random seed to <N>
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:conf Show current generation config
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:conf <key>=<value> Change generation config
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:reset-conf Reset generation config
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"""
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_ALL_COMMAND_NAMES = [
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"help",
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"h",
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"exit",
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"quit",
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"q",
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"clear",
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"cl",
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"clear-history",
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"clh",
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"history",
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"his",
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"seed",
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"conf",
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"reset-conf",
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]
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def _setup_readline():
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try:
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import readline
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except ImportError:
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return
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_matches = []
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def _completer(text, state):
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nonlocal _matches
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if state == 0:
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_matches = [
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cmd_name for cmd_name in _ALL_COMMAND_NAMES if cmd_name.startswith(text)
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]
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if 0 <= state < len(_matches):
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return _matches[state]
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return None
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readline.set_completer(_completer)
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readline.parse_and_bind("tab: complete")
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def _load_model_tokenizer(args):
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tokenizer = AutoTokenizer.from_pretrained(
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args.checkpoint_path,
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resume_download=True,
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)
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# Set bos_token for left-padding
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if tokenizer.pad_token is None:
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tokenizer.pad_token = tokenizer.bos_token
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device_map = "cpu" if args.cpu_only else "auto"
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model = AutoModelForCausalLM.from_pretrained(
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args.checkpoint_path,
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torch_dtype="auto",
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device_map=device_map,
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resume_download=True,
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).eval()
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# Conservative generation config
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model.generation_config.max_new_tokens = 256
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model.generation_config.temperature = 0.7
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model.generation_config.top_k = 50
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model.generation_config.top_p = 0.9
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model.generation_config.pad_token_id = tokenizer.pad_token_id
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model.generation_config.eos_token_id = tokenizer.eos_token_id
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model.generation_config.do_sample = False
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return model, tokenizer
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def _gc():
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import gc
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gc.collect()
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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def _clear_screen():
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if platform.system() == "Windows":
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os.system("cls")
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else:
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os.system("clear")
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def _print_history(history):
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terminal_width = shutil.get_terminal_size()[0]
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print(f"History ({len(history)})".center(terminal_width, "="))
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for index, (query, response) in enumerate(history):
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print(f"User[{index}]: {query}")
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print(f"Qwen[{index}]: {response}")
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print("=" * terminal_width)
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def _get_input() -> str:
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while True:
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try:
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message = input("User> ").strip()
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except UnicodeDecodeError:
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print("[ERROR] Encoding error in input")
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continue
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except KeyboardInterrupt:
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exit(1)
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if message:
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return message
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print("[ERROR] Query is empty")
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def _chat_stream(model, tokenizer, query, history):
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conversation = []
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for query_h, response_h in history:
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conversation.append({"role": "user", "content": query_h})
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conversation.append({"role": "assistant", "content": response_h})
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conversation.append({"role": "user", "content": query})
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input_text = tokenizer.apply_chat_template(
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conversation,
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add_generation_prompt=True,
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tokenize=False,
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)
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# Perform left-padding with bos_token
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inputs = tokenizer(
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[input_text],
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return_tensors="pt",
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padding="longest",
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truncation=True,
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pad_to_multiple_of=8,
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max_length=1024,
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add_special_tokens=False
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).to(model.device)
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# Update attention_mask for left-padding compatibility
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inputs["attention_mask"] = inputs["attention_mask"].flip(dims=[1])
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streamer = TextIteratorStreamer(
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tokenizer=tokenizer, skip_prompt=True, timeout=60.0, skip_special_tokens=True
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)
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generation_kwargs = {
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**inputs,
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"streamer": streamer,
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}
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thread = Thread(target=model.generate, kwargs=generation_kwargs)
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thread.start()
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for new_text in streamer:
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yield new_text
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def main():
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parser = argparse.ArgumentParser(
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description="Qwen2.5-Instruct command-line interactive chat demo."
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)
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parser.add_argument(
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"-c",
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"--checkpoint-path",
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type=str,
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default=DEFAULT_CKPT_PATH,
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help="Checkpoint name or path, default to %(default)r",
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)
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parser.add_argument("-s", "--seed", type=int, default=1234, help="Random seed")
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parser.add_argument(
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"--cpu-only", action="store_true", help="Run demo with CPU only"
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)
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args = parser.parse_args()
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history, response = [], ""
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model, tokenizer = _load_model_tokenizer(args)
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orig_gen_config = deepcopy(model.generation_config)
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_setup_readline()
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_clear_screen()
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print(_WELCOME_MSG)
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seed = args.seed
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while True:
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query = _get_input()
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# Process commands.
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if query.startswith(":"):
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command_words = query[1:].strip().split()
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if not command_words:
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command = ""
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else:
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command = command_words[0]
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if command in ["exit", "quit", "q"]:
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break
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elif command in ["clear", "cl"]:
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_clear_screen()
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print(_WELCOME_MSG)
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_gc()
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continue
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elif command in ["clear-history", "clh"]:
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print(f"[INFO] All {len(history)} history cleared")
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history.clear()
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_gc()
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continue
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elif command in ["help", "h"]:
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print(_HELP_MSG)
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continue
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elif command in ["history", "his"]:
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_print_history(history)
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continue
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elif command in ["seed"]:
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if len(command_words) == 1:
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print(f"[INFO] Current random seed: {seed}")
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continue
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else:
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new_seed_s = command_words[1]
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try:
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new_seed = int(new_seed_s)
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except ValueError:
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print(
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f"[WARNING] Fail to change random seed: {new_seed_s!r} is not a valid number"
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)
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else:
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print(f"[INFO] Random seed changed to {new_seed}")
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seed = new_seed
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continue
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elif command in ["conf"]:
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if len(command_words) == 1:
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print(model.generation_config)
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else:
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for key_value_pairs_str in command_words[1:]:
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eq_idx = key_value_pairs_str.find("=")
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if eq_idx == -1:
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print("[WARNING] format: <key>=<value>")
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continue
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conf_key, conf_value_str = (
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key_value_pairs_str[:eq_idx],
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key_value_pairs_str[eq_idx + 1 :],
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)
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try:
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conf_value = eval(conf_value_str)
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except Exception as e:
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print(e)
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continue
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else:
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print(
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f"[INFO] Change config: model.generation_config.{conf_key} = {conf_value}"
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)
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setattr(model.generation_config, conf_key, conf_value)
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continue
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elif command in ["reset-conf"]:
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print("[INFO] Reset generation config")
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model.generation_config = deepcopy(orig_gen_config)
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print(model.generation_config)
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continue
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# Run chat.
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set_seed(seed)
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_clear_screen()
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print(f"\nUser: {query}")
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print(f"\nQwen: ", end="")
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try:
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partial_text = ""
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for new_text in _chat_stream(model, tokenizer, query, history):
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print(new_text, end="", flush=True)
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partial_text += new_text
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response = partial_text
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print()
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except KeyboardInterrupt:
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print("[WARNING] Generation interrupted")
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continue
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history.append((query, response))
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if __name__ == "__main__":
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main()
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34
Backend/test_flant5.py
Normal file
34
Backend/test_flant5.py
Normal file
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import requests
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API_TOKEN = "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"
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API_URL = "https://api-inference.huggingface.co/models/google/flan-t5-large"
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headers = {
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"Authorization": f"Bearer {API_TOKEN}",
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"Content-Type": "application/json"
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}
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def query_flan_t5(prompt):
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payload = {
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"inputs": prompt,
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"parameters": {
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"max_length": 250,
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"do_sample": True,
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"temperature": 0.9,
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"top_p": 0.95,
|
||||
"top_k": 50
|
||||
}
|
||||
}
|
||||
response = requests.post(API_URL, headers=headers, json=payload)
|
||||
return response.json()
|
||||
|
||||
|
||||
prompt = "Ako sa máš? Daj odpoved v slovencine"
|
||||
result = query_flan_t5(prompt)
|
||||
|
||||
if isinstance(result, list) and len(result) > 0:
|
||||
print("Ответ от Flan-T5:", result[0]['generated_text'])
|
||||
else:
|
||||
print("Ошибка при получении ответа:", result)
|
||||
47
Backend/test_mt5_base.py
Normal file
47
Backend/test_mt5_base.py
Normal file
@ -0,0 +1,47 @@
|
||||
# import requests
|
||||
#
|
||||
# API_TOKEN = "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"
|
||||
# API_URL = "https://api-inference.huggingface.co/models/google/mt5-base"
|
||||
#
|
||||
# headers = {
|
||||
# "Authorization": f"Bearer {API_TOKEN}",
|
||||
# "Content-Type": "application/json"
|
||||
# }
|
||||
#
|
||||
# def query_mT5(prompt):
|
||||
# payload = {
|
||||
# "inputs": prompt,
|
||||
# "parameters": {
|
||||
# "max_length": 100,
|
||||
# "do_sample": True,
|
||||
# "temperature": 0.7
|
||||
# }
|
||||
# }
|
||||
# response = requests.post(API_URL, headers=headers, json=payload)
|
||||
# return response.json()
|
||||
#
|
||||
# # Пример использования
|
||||
# result = query_mT5("Aké sú účinné lieky na horúčku?")
|
||||
# print("Ответ от mT5:", result)
|
||||
|
||||
from transformers import AutoTokenizer, MT5ForConditionalGeneration
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained("google/mt5-small")
|
||||
model = MT5ForConditionalGeneration.from_pretrained("google/mt5-small")
|
||||
|
||||
# training
|
||||
input_ids = tokenizer("The <extra_id_0> walks in <extra_id_1> park", return_tensors="pt").input_ids
|
||||
labels = tokenizer("<extra_id_0> cute dog <extra_id_1> the <extra_id_2>", return_tensors="pt").input_ids
|
||||
outputs = model(input_ids=input_ids, labels=labels)
|
||||
loss = outputs.loss
|
||||
logits = outputs.logits
|
||||
|
||||
# inference
|
||||
|
||||
input_ids = tokenizer(
|
||||
"summarize: studies have shown that owning a dog is good for you", return_tensors="pt"
|
||||
).input_ids # Batch size 1
|
||||
outputs = model.generate(input_ids, max_new_tokens=50)
|
||||
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
|
||||
|
||||
# studies have shown that owning a dog is good for you.
|
||||
19
Backend/test_slovakbert-skquad.py
Normal file
19
Backend/test_slovakbert-skquad.py
Normal file
@ -0,0 +1,19 @@
|
||||
from sentence_transformers import SentenceTransformer, util
|
||||
|
||||
# Загрузка модели из Hugging Face
|
||||
model = SentenceTransformer("TUKE-DeutscheTelekom/slovakbert-skquad-mnlr") # Замените на ID нужной модели
|
||||
|
||||
# Пример предложений на словацком языке
|
||||
sentences = [
|
||||
"Prvý most cez Zlatý roh nechal vybudovať cisár Justinián I. V roku 1502 vypísal sultán Bajezid II. súťaž na nový most.",
|
||||
"V ktorom roku vznikol druhý drevený most cez záliv Zlatý roh?",
|
||||
"Aká je priemerná dĺžka života v Eritrei?"
|
||||
]
|
||||
|
||||
# Получение эмбеддингов для каждого предложения
|
||||
embeddings = model.encode(sentences)
|
||||
print("Shape of embeddings:", embeddings.shape) # Вывод формы эмбеддингов, например (3, 768)
|
||||
|
||||
# Вычисление сходства между предложениями
|
||||
similarities = util.cos_sim(embeddings, embeddings)
|
||||
print("Similarity matrix:\n", similarities)
|
||||
Loading…
Reference in New Issue
Block a user