trainer/sft_train.py

import os
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments
from trl import SFTTrainer
from peft import LoraConfig

BASE_MODEL = os.environ.get("BASE_MODEL", "Qwen/Qwen2.5-0.5B-Instruct")

def main():
    ds = load_dataset("json", data_files="data/sft.jsonl")["train"]

    tok = AutoTokenizer.from_pretrained(BASE_MODEL, use_fast=True)
    if tok.pad_token is None:
        tok.pad_token = tok.eos_token

    model = AutoModelForCausalLM.from_pretrained(
        BASE_MODEL,
        device_map="auto",
        torch_dtype="auto",
    )

    peft_cfg = LoraConfig(
        r=16, lora_alpha=32, lora_dropout=0.05,
        bias="none", task_type="CAUSAL_LM",
        target_modules=["q_proj","k_proj","v_proj","o_proj","up_proj","down_proj","gate_proj"]
    )

    args = TrainingArguments(
        output_dir="adapter_sft",
        per_device_train_batch_size=2,
        gradient_accumulation_steps=8,
        learning_rate=2e-4,
        num_train_epochs=1,
        logging_steps=20,
        save_steps=200,
        fp16=True,
        report_to="none"
    )

    trainer = SFTTrainer(
        model=model,
        tokenizer=tok,
        train_dataset=ds,
        peft_config=peft_cfg,
        max_seq_length=1024,
        args=args,
        packing=False,
        dataset_text_field=None,  # because we use "messages"
    )
    trainer.train()
    trainer.save_model("adapter_sft")
    tok.save_pretrained("adapter_sft")
    print("Saved adapter_sft/")

if __name__ == "__main__":
    main()