| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
|
|
| """ |
| 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 |
|
|
| |
| 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", |
| ) |
|
|
| |
| 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") |
|
|