train_glm_qlora_v9.py

# /// script
# requires-python = ">=3.10"
# dependencies = [
#     "trl>=0.12.0",
#     "peft>=0.7.0",
#     "transformers @ git+https://github.com/huggingface/transformers.git",
#     "accelerate @ git+https://github.com/huggingface/accelerate.git",
#     "bitsandbytes>=0.45.0",
#     "trackio",
#     "datasets",
# ]
# ///

"""
Agent Zero SFT: zai-org/GLM-4.7-Flash (30B MoE)
QLoRA (4-bit) — NO CPU offload, let it fit entirely on GPU.
30B MoE in 4-bit NF4 ≈ 15-17GB, fits in 24GB a10g-large.
"""

import os
import torch
import trackio
from huggingface_hub import login
login(token=os.environ["HF_TOKEN"])

from datasets import load_dataset
from peft import LoraConfig
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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)}")

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

print("Loading model in 4-bit (GPU only, no CPU offload)...")
model = AutoModelForCausalLM.from_pretrained(
    "zai-org/GLM-4.7-Flash",
    quantization_config=bnb_config,
    trust_remote_code=True,
    device_map="auto",
    torch_dtype=torch.bfloat16,
)
tokenizer = AutoTokenizer.from_pretrained("zai-org/GLM-4.7-Flash", trust_remote_code=True)
print("Model loaded.")

if hasattr(model, 'hf_device_map'):
    devices = {}
    for v in model.hf_device_map.values():
        devices[str(v)] = devices.get(str(v), 0) + 1
    print(f"Device distribution: {devices}")

import subprocess
result = subprocess.run(['nvidia-smi'], capture_output=True, text=True)
print(result.stdout)

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-qlora-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=model,
    tokenizer=tokenizer,
    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!")