# /// script # requires-python = ">=3.10" # dependencies = [ # "trl>=0.12.0", # "peft>=0.7.0", # "transformers>=4.36.0", # "accelerate>=0.24.0", # "trackio", # "datasets", # ] # /// """ Agent Zero SFT: zai-org/GLM-4.7-Flash (30B MoE) LoRA fine-tuning on agent-zero-sft-v1 dataset. Router layers frozen - only attention layers trained. """ import trackio from datasets import load_dataset from peft import LoraConfig from trl import SFTTrainer, SFTConfig # Load dataset print("Loading dataset...") train_ds = load_dataset("wheattoast11/agent-zero-sft-v1", data_files="data/train.jsonl", split="train") val_ds = load_dataset("wheattoast11/agent-zero-sft-v1", data_files="data/validation.jsonl", split="train") print(f"Train: {len(train_ds)}, Val: {len(val_ds)}") config = SFTConfig( output_dir="agent-zero-glm-4.7-v1", push_to_hub=True, hub_model_id="wheattoast11/agent-zero-glm-4.7-v1", hub_strategy="every_save", hub_private_repo=True, num_train_epochs=2, per_device_train_batch_size=1, gradient_accumulation_steps=16, learning_rate=1e-4, bf16=True, gradient_checkpointing=True, logging_steps=10, save_strategy="steps", save_steps=50, save_total_limit=2, eval_strategy="steps", eval_steps=50, warmup_ratio=0.1, lr_scheduler_type="cosine", report_to="trackio", project="agent-zero-finetune", run_name="glm-4.7-flash-sft-v1", ) # LoRA targeting attention layers only (router layers frozen) peft_config = LoraConfig( r=16, lora_alpha=32, lora_dropout=0.05, bias="none", task_type="CAUSAL_LM", target_modules=["q_proj", "v_proj", "k_proj", "o_proj"], ) print("Initializing trainer...") trainer = SFTTrainer( model="zai-org/GLM-4.7-Flash", train_dataset=train_ds, eval_dataset=val_ds, args=config, peft_config=peft_config, ) print("Starting training...") trainer.train() print("Pushing to Hub...") trainer.push_to_hub() trackio.finish() print("Done! Model at: https://huggingface.co/wheattoast11/agent-zero-glm-4.7-v1")