ably.do/herbert.py

import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"

import torch
import faiss
import numpy as np
from sentence_transformers import SentenceTransformer
from datasets import Dataset
from peft import LoraConfig, get_peft_model, PeftModel
from transformers import (AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer, 
                          DataCollatorForLanguageModeling, LlamaTokenizer, LlamaForCausalLM)
import bitsandbytes as bnb

# 1️⃣ Inicjalizacja modelu do embeddingów
embed_model = SentenceTransformer("all-MiniLM-L6-v2")

# 2️⃣ Wczytanie dokumentów i embeddingów
def read_documents_from_file(file_path):
    with open(file_path, 'r', encoding='utf-8') as file:
        content = file.read()
        articles = content.split('\n\n')
        return [article.strip() for article in articles if article.strip().startswith('Art.')]

file_path = './docs/kodekspracy.txt'  # Zmień na właściwą ścieżkę
documents = read_documents_from_file(file_path)
embeddings = embed_model.encode(documents)

# 3️⃣ Inicjalizacja FAISS i dodanie wektorów
dim = embeddings.shape[1]
index = faiss.IndexFlatL2(dim)
index.add(np.array(embeddings, dtype=np.float32))

# 4️⃣ Przygotowanie danych treningowych
def create_training_data():
    return Dataset.from_dict({"text": documents, "embedding": embeddings.tolist()})

dataset = create_training_data()
split_dataset = dataset.train_test_split(test_size=0.25)
train_dataset, eval_dataset = split_dataset["train"], split_dataset["test"]

# 5️⃣ Ładowanie modelu bazowego i fine-tunowanego
base_model = "decapoda-research/llama-7b-hf"
finetuned_model = "mmosiolek/polpaca-lora-7b"

tokenizer = LlamaTokenizer.from_pretrained(base_model)
tokenizer.pad_token_id = 0
tokenizer.padding_side = "left"

model = LlamaForCausalLM.from_pretrained(base_model, torch_dtype=torch.float16).to("cuda")
model = PeftModel.from_pretrained(model, finetuned_model).to("cuda")

# 6️⃣ Konfiguracja LoRA
lora_config = LoraConfig(
    r=8, lora_alpha=32, lora_dropout=0.1, bias="none", task_type="CAUSAL_LM")
model = get_peft_model(model, lora_config)

# 7️⃣ Tokenizacja
def tokenize_function(examples):
    return tokenizer(examples["text"], padding="max_length", truncation=True, max_length=384)

tokenized_train = train_dataset.map(tokenize_function, batched=True)
tokenized_eval = eval_dataset.map(tokenize_function, batched=True)

# 8️⃣ Parametry treningu
training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="steps",
    eval_steps=500,
    save_strategy="steps",
    save_steps=500,
    learning_rate=1e-5,
    per_device_train_batch_size=2,
    per_device_eval_batch_size=2,
    num_train_epochs=16,
    weight_decay=0.01,
    load_best_model_at_end=True,
    metric_for_best_model="loss",
    greater_is_better=False,
)

# 9️⃣ Data Collator
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)

# 🔟 Trening
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_train,
    eval_dataset=tokenized_eval,
    data_collator=data_collator,
)
trainer.train()

# 1️⃣1️⃣ Zapis modelu lokalnie
model.save_pretrained("./models/finetuned_llama")
tokenizer.save_pretrained("./models/finetuned_llama")

print("✅ Model został wytrenowany i zapisany!")