train_manny.py

# /// script
# dependencies = ["trl>=0.12.0", "peft>=0.7.0", "trackio", "datasets", "accelerate", "bitsandbytes"]
# ///

from datasets import load_dataset
from peft import LoraConfig
from trl import SFTTrainer, SFTConfig

dataset = load_dataset("NTA-Dev/bf-manny-training", split="train")
print(f"Loaded {len(dataset)} examples for Manny")

trainer = SFTTrainer(
    model="Qwen/Qwen2.5-3B-Instruct",
    train_dataset=dataset,
    peft_config=LoraConfig(
        r=32, lora_alpha=64,
        target_modules=["q_proj","k_proj","v_proj","o_proj","gate_proj","up_proj","down_proj"],
        lora_dropout=0.05,
    ),
    args=SFTConfig(
        output_dir="manny-agent",
        push_to_hub=True,
        hub_model_id="NTA-Dev/bf-manny-agent",
        num_train_epochs=5,
        per_device_train_batch_size=2,
        gradient_accumulation_steps=4,
        learning_rate=2e-4,
        warmup_ratio=0.1,
        logging_steps=5,
        save_strategy="epoch",
        bf16=True,
        gradient_checkpointing=True,
        max_length=2048,
    )
)
trainer.train()
trainer.push_to_hub()
print("Manny training complete!")