evo/training_pipeline.py

"""
Agent Q3 [Evo] — Training Pipeline
Unsloth LoRA fine-tuning on Llama-3.2-3B-Instruct.
Reads from HF dataset madDegen/agent-q3, pushes adapter to madDegen/agent-q3-loras.
"""
import os
from datasets import load_dataset
from unsloth import FastLanguageModel
from trl import SFTTrainer
from transformers import TrainingArguments

BASE_MODEL   = os.getenv("BASE_MODEL",  "unsloth/Llama-3.2-3B-Instruct-bnb-4bit")
HF_DATASET   = os.getenv("HF_DATASET",  "madDegen/agent-q3")
ADAPTER_REPO = os.getenv("ADAPTER_REPO","madDegen/agent-q3-loras")
MAX_SEQ_LEN  = int(os.getenv("MAX_SEQ_LEN", 2048))
LORA_RANK    = int(os.getenv("LORA_RANK", 16))

def run():
    model, tokenizer = FastLanguageModel.from_pretrained(
        model_name=BASE_MODEL,
        max_seq_length=MAX_SEQ_LEN,
        load_in_4bit=True,
    )
    model = FastLanguageModel.get_peft_model(
        model,
        r=LORA_RANK,
        target_modules=["q_proj","k_proj","v_proj","o_proj","gate_proj","up_proj","down_proj"],
        lora_alpha=16,
        lora_dropout=0,
        bias="none",
        use_gradient_checkpointing="unsloth",
    )

    dataset = load_dataset(HF_DATASET, split="train")

    trainer = SFTTrainer(
        model=model,
        tokenizer=tokenizer,
        train_dataset=dataset,
        dataset_text_field="text",
        max_seq_length=MAX_SEQ_LEN,
        args=TrainingArguments(
            per_device_train_batch_size=2,
            gradient_accumulation_steps=4,
            warmup_steps=10,
            max_steps=100,
            learning_rate=2e-4,
            fp16=True,
            logging_steps=10,
            output_dir="./evo_checkpoints",
            optim="adamw_8bit",
            seed=42,
        ),
    )
    trainer.train()
    model.push_to_hub(ADAPTER_REPO, token=os.getenv("HF_TOKEN"))
    tokenizer.push_to_hub(ADAPTER_REPO, token=os.getenv("HF_TOKEN"))
    print(f"Adapter pushed to {ADAPTER_REPO}")

if __name__ == "__main__":
    run()