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train_manny.py

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+# /// 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!")