train.py

import unsloth
from unsloth import FastLanguageModel

import os
import zipfile
import pandas as pd
from datasets import Dataset
import torch
from transformers import TrainingArguments
from trl import SFTTrainer


def main():
    # 1) Load 4-bit model + tokenizer (SmolLM already chat-formatted)
    model_name = "HuggingFaceTB/SmolLM2-1.7B"
    max_seq_length = 768
    dtype = torch.float16
    load_in_4bit = True

    model, tokenizer = FastLanguageModel.from_pretrained(
        model_name=model_name,
        max_seq_length=max_seq_length,
        dtype=dtype,
        load_in_4bit=load_in_4bit,
    )

    # 2) NO manual special-tokens injection or resizing — base model tokenizer already includes chat markers

    # 3) Load chat-formatted dataset
    df = pd.read_json("data.jsonl", lines=True)
    # Confirm each sample ends with the end-of-turn token
    assert df['text'].str.endswith("<|im_end|>").all(), "Some samples missing end-of-turn token"

    # 4) Create Hugging Face Dataset and split
    full_dataset = Dataset.from_pandas(df[["text"]])
    split = full_dataset.train_test_split(test_size=0.15, seed=42)
    train_dataset = split["train"]
    eval_dataset = split["test"]
    print(f"✅ Training samples: {len(train_dataset)} | Eval samples: {len(eval_dataset)}")

    # 5) Apply LoRA adapters
    model = FastLanguageModel.get_peft_model(
        model,
        r=8,
        lora_alpha=32,
        lora_dropout=0.05,
        bias="none",
        target_modules=[
            "q_proj", "k_proj", "v_proj", "o_proj",
            "gate_proj", "up_proj", "down_proj",
        ],
        use_gradient_checkpointing=True,
    )

    # 6) Tokenization function
    def tokenize_fn(examples):
        tokens = tokenizer(
            examples["text"],
            truncation=True,
            padding="max_length",
            max_length=max_seq_length,
        )
        tokens["labels"] = tokens["input_ids"].copy()
        return tokens

    tokenized_train = train_dataset.map(tokenize_fn, batched=True, remove_columns=["text"])
    tokenized_eval = eval_dataset.map(tokenize_fn, batched=True, remove_columns=["text"])

    # 7) Training arguments
    training_args = TrainingArguments(
        output_dir="./output_model",
        per_device_train_batch_size=8,
        gradient_accumulation_steps=1,
        fp16=True,
        num_train_epochs=3,
        learning_rate=2e-4,
        logging_strategy="steps",
        logging_steps=25,
        save_strategy="epoch",
        save_total_limit=2,
        eval_strategy="epoch",
        load_best_model_at_end=True,
        metric_for_best_model="eval_loss",
        greater_is_better=False,
        dataloader_num_workers=2,
    )

    # 8) Train with SFTTrainer
    trainer = SFTTrainer(
        model=model,
        tokenizer=tokenizer,
        args=training_args,
        train_dataset=tokenized_train,
        eval_dataset=tokenized_eval,
    )

    print("🚀 Starting training...")
    trainer.train()
    print("✅ Training complete.")

    # 9) Save and zip model
    final_dir = "./output_model_final"
    os.makedirs(final_dir, exist_ok=True)
    model.save_pretrained(final_dir)
    tokenizer.save_pretrained(final_dir)

    zip_path = "model.zip"
    print(f"🗜 Zipping model → {zip_path}")
    with zipfile.ZipFile(zip_path, "w", zipfile.ZIP_DEFLATED) as zf:
        for root, _, files in os.walk(final_dir):
            for fname in files:
                full = os.path.join(root, fname)
                rel = os.path.relpath(full, final_dir)
                zf.write(full, rel)
    print(f"✅ Model zipped → {zip_path}")

if __name__ == "__main__":
    main()