119 lines
3.8 KiB
Python
119 lines
3.8 KiB
Python
import os
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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import torch
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import faiss
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import numpy as np
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from sentence_transformers import SentenceTransformer
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from datasets import Dataset
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from peft import LoraConfig, get_peft_model
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from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer, DataCollatorForLanguageModeling
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# 1️⃣ Inicjalizacja modelu do embeddingów
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embed_model = SentenceTransformer("all-MiniLM-L6-v2")
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# 2️⃣ Dodanie dokumentów i embeddingów
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def read_documents_from_file(file_path):
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with open(file_path, 'r', encoding='utf-8') as file:
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content = file.read()
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articles = content.split('\n\n')
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documents = []
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for article in articles:
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if article.strip().startswith('Art.'):
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documents.append(article.strip())
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return documents
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#documents = [
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# "Jak założyć firmę w Polsce?",
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# "Jak rozliczyć podatek VAT?",
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# "Procedura składania reklamacji w e-sklepie.",
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# "Jakie dokumenty są potrzebne do rejestracji działalności?"
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#]
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file_path = './docs/kodekspracy.txt' # Zmień na właściwą ścieżkę
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documents = read_documents_from_file(file_path)
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embeddings = embed_model.encode(documents)
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# 3️⃣ Inicjalizacja FAISS i dodanie wektorów
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dim = embeddings.shape[1]
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index = faiss.IndexFlatL2(dim)
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index.add(np.array(embeddings, dtype=np.float32))
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# 4️⃣ Przygotowanie danych treningowych
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def create_training_data():
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data = {
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"text": documents,
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"embedding": embeddings.tolist()
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}
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return Dataset.from_dict(data)
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dataset = create_training_data()
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# Podział danych na treningowe i ewaluacyjne
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split_dataset = dataset.train_test_split(test_size=0.25)
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train_dataset = split_dataset["train"]
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eval_dataset = split_dataset["test"]
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# 5️⃣ Ładowanie modelu Gemma 2B
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model_name = "Lajonbot/vicuna-7b-v1.5-PL-lora_unload"
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16).to(device)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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# 6️⃣ Konfiguracja LoRA
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lora_config = LoraConfig(
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r=8, lora_alpha=32, lora_dropout=0.1, bias="none", task_type="CAUSAL_LM"
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)
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model = get_peft_model(model, lora_config)
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# 7️⃣ Tokenizacja danych
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max_length = 384
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def tokenize_function(examples):
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return tokenizer(
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examples["text"],
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padding="max_length",
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truncation=True,
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max_length=max_length
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)
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tokenized_train = train_dataset.map(tokenize_function, batched=True)
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tokenized_eval = eval_dataset.map(tokenize_function, batched=True)
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# 8️⃣ Parametry treningu
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training_args = TrainingArguments(
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output_dir="./results",
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eval_strategy="steps", # Ewaluacja co określoną liczbę kroków
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eval_steps=500, # Ewaluacja co 500 kroków
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save_strategy="steps", # Zapis modelu co określoną liczbę kroków
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save_steps=500, # Zapis modelu co 500 kroków
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learning_rate=1e-5,
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per_device_train_batch_size=2,
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per_device_eval_batch_size=2,
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num_train_epochs=16,
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weight_decay=0.01,
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load_best_model_at_end=True, # Wczytaj najlepszy model na końcu
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metric_for_best_model="loss", # Kryterium wyboru najlepszego modelu
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greater_is_better=False, # Niższy loss = lepszy model
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)
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# 9️⃣ Data Collator
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data_collator = DataCollatorForLanguageModeling(
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tokenizer=tokenizer,
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mlm=False
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)
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# 🔟 Trening modelu
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trainer = Trainer(
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model=model,
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args=training_args,
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train_dataset=tokenized_train,
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eval_dataset=tokenized_eval, # Dodany zestaw ewaluacyjny
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data_collator=data_collator,
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)
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trainer.train()
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# 1️⃣1️⃣ Zapis modelu
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model.save_pretrained("./models/herbert")
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tokenizer.save_pretrained("./models/herbert")
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print("✅ Model został wytrenowany i zapisany!") |