train_coding_sft.py

import os
import torch
from datasets import load_dataset
from peft import LoraConfig
from trl import SFTTrainer, SFTConfig
from transformers import BitsAndBytesConfig

DATA_ID = os.environ.get("CODING_DATA", "theblackcat102/evol-codealpaca-v1")
N_ROWS = int(os.environ.get("CODING_ROWS", "60000"))

dataset = load_dataset(DATA_ID, split="train")
ds = dataset.shuffle(seed=42).select(range(N_ROWS))
ds = ds.train_test_split(test_size=0.05, seed=42)


def fmt(rows):
    texts = []
    for i, o in zip(rows["instruction"], rows["output"]):
        texts.append(f"### Instruction\n{i}\n\n### Response\n{o}<|endoftext|>")
    return {"text": texts}


train_ds = ds["train"].map(fmt, batched=True, remove_columns=ds["train"].column_names)
eval_ds = ds["test"].map(fmt, batched=True, remove_columns=ds["test"].column_names)

bnb = BitsAndBytesConfig(
    load_in_4bit=True, bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=torch.float16,
)

trainer = SFTTrainer(
    model="Qwen/Qwen2.5-Coder-0.5B",
    train_dataset=train_ds,
    eval_dataset=eval_ds,
    peft_config=LoraConfig(r=16, lora_alpha=32, target_modules=["q_proj","k_proj","v_proj","o_proj"], lora_dropout=0.05),
    args=SFTConfig(
        output_dir="autoresearch-coding-v1",
        push_to_hub=True,
        hub_model_id="40Hz/autoresearch-coding-v1",
        num_train_epochs=2,
        per_device_train_batch_size=4,
        gradient_accumulation_steps=4,
        gradient_checkpointing=True,
        eval_strategy="steps",
        eval_steps=200,
        logging_steps=20,
        save_steps=200,
        save_total_limit=3,
        fp16=True,
        learning_rate=2e-4,
        lr_scheduler_type="cosine",
        warmup_ratio=0.05,
        optim="paged_adamw_8bit",
        max_length=1024,
        report_to="trackio",
        trackio_space_id=os.environ.get("TRACKIO_SPACE_ID", "40Hz/autoresearch-gpu"),
        model_init_kwargs={"quantization_config": bnb, "torch_dtype": torch.float16},
    ),
)

trainer.train()
trainer.push_to_hub()
print("DONE coding-v1 pushed to 40Hz/autoresearch-coding-v1")