File size: 3,763 Bytes
5a72b92 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | import os
import subprocess
import sys
def run_cmd(cmd, cwd=None):
print(f"$ {cmd}")
result = subprocess.run(cmd, shell=True, cwd=cwd, capture_output=True, text=True)
if result.stdout:
print(result.stdout)
if result.stderr:
print(result.stderr, file=sys.stderr)
if result.returncode != 0:
raise RuntimeError(f"Command failed: {cmd}")
return result
def main():
os.makedirs("/content/project_bob", exist_ok=True)
os.chdir("/content/project_bob")
run_cmd("pip install -q -U autotrain-advanced huggingface_hub huggingface_hub[cli] optimum transformers accelerate peft bitsandbytes datasets")
with open("dataset.jsonl", "w") as f:
f.write("""{"text": "<|begin_of_text|><|start_header_id|>user<|end_header_id|>What is the current price of Bitcoin?<|eot_id|><|start_header_id|>assistant<|end_header_id|><call_tool>get_crypto_price({\\"symbol\\": \\"BTC\\"})</call_tool><|eot_id|>"}
{"text": "<|begin_of_text|><|start_header_id|>user<|end_header_id|>Multiply 42 by 58 for me.<|eot_id|><|start_header_id|>assistant<|end_header_id|><call_tool>multiply_numbers({\\"a\\": 42, \\"b\\": 58})</call_tool><|eot_id|>"}
{"text": "<|begin_of_text|><|start_header_id|>user<|end_header_id|>What is the capital of France?<|eot_id|><|start_header_id|>assistant<|end_header_id|>The capital of France is Paris.<|eot_id|>"}
{"text": "<|begin_of_text|><|start_header_id|>user<|end_header_id|>Calculate 144 multiplied by 37.<|eot_id|><|start_header_id|>assistant<|end_header_id|><call_tool>multiply_numbers({\\"a\\": 144, \\"b\\": 37})</call_tool><|eot_id|>"}
{"text": "<|begin_of_text|><|start_header_id|>user<|end_header_id|>What's the current Ethereum price?<|eot_id|><|start_header_id|>assistant<|end_header_id|><call_tool>get_crypto_price({\\"symbol\\": \\"ETH\\"})</call_tool><|eot_id|>"}
""")
print("Uploading dataset.jsonl to Hugging Face Hub...")
from huggingface_hub import HfApi, HfFolder
api = HfApi()
api.upload_file(
path_or_fileobj="dataset.jsonl",
path_in_repo="dataset.jsonl",
repo_id="yummyfiles/Bob",
repo_type="dataset",
token=os.getenv("HF_TOKEN"),
)
print("Dataset uploaded to yummyfiles/Bob")
print("Starting AutoTrain QLoRA fine-tuning...")
autotrain_cmd = (
"autotrain llm --train "
"--project-name Bob "
"--model meta-llama/Llama-3.2-1B-Instruct "
"--train-data yummyfiles/Bob "
"--data-path dataset.jsonl "
"--text-column text "
"--trainer peft "
"--peft-r 16 "
"--peft-alpha 32 "
"--peft-dropout 0.05 "
"--learning-rate 2e-4 "
"--batch-size 1 "
"--epochs 3 "
"--block-size 2048 "
"--warmup-ratio 0.03 "
"--optimizer paged_adamw_8bit "
"--scheduler cosine "
"--gradient-accumulation 4 "
"--mixed-precision bf16 "
"--quantization 4bit "
"--push-to-hub "
"--repo-id yummyfiles/Bob "
f"--token {os.getenv('HF_TOKEN')}"
)
run_cmd(autotrain_cmd)
print("Converting model to ONNX (4-bit)...")
run_cmd("pip install -q optimum[onnxruntime]")
run_cmd(
"optimum-cli export onnx "
"--model yummyfiles/Bob "
"--task text-generation "
"--quantize int4 "
"--output /content/project_bob/web_model"
)
print("Uploading ONNX model to yummyfiles/Bob...")
api.upload_folder(
folder_path="/content/project_bob/web_model",
path_in_repo="web_model",
repo_id="yummyfiles/Bob",
repo_type="model",
token=os.getenv("HF_TOKEN"),
)
print("Done! Model and ONNX artifacts uploaded to yummyfiles/Bob")
if __name__ == "__main__":
main() |