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| """ |
| Merge LoRA Adapter and Push to Hub (HF Cloud) |
| |
| This script is designed to run on Hugging Face Jobs infrastructure. |
| It loads a base model + LoRA adapter, merges them, and pushes the |
| standalone merged model to the Hub. |
| |
| Usage: |
| # Submit via Makefile |
| make merge-cloud |
| |
| # Or submit directly |
| python scripts/submit_cloud_training.py \ |
| --flavor a100x4 --timeout 2h \ |
| --script scripts/merge_hf_cloud.py |
| """ |
|
|
| import os |
|
|
| import torch |
| from huggingface_hub import login |
| from peft import PeftModel |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
|
|
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| |
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|
|
| BASE_MODEL = "Qwen/Qwen3-Coder-Next" |
| ADAPTER_MODEL = "yangluo/univer-api-qwen3-next-coder" |
| MERGED_MODEL_ID = "yangluo/univer-api-qwen3-next-coder-merged" |
|
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|
|
| def main(): |
| |
| hf_token = os.environ.get("HF_TOKEN") |
| if hf_token: |
| print("Logging in to Hugging Face Hub...") |
| login(token=hf_token, add_to_git_credential=False) |
| print(" Login successful!") |
| else: |
| print("ERROR: HF_TOKEN not found in environment!") |
| print("Cannot push merged model without authentication.") |
| return 1 |
|
|
| print("=" * 60) |
| print(" MERGE LoRA ADAPTER & PUSH TO HUB") |
| print("=" * 60) |
| print(f"Base model: {BASE_MODEL}") |
| print(f"LoRA adapter: {ADAPTER_MODEL}") |
| print(f"Merged output: {MERGED_MODEL_ID}") |
| print(f"Dtype: bfloat16 (full precision, no quantization)") |
| print("=" * 60) |
|
|
| |
| |
| |
| print("\n[1/5] Loading tokenizer...") |
| tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True) |
| print(f" Tokenizer loaded: {tokenizer.__class__.__name__}") |
|
|
| |
| |
| |
| print(f"\n[2/5] Loading base model: {BASE_MODEL}") |
| print(" Loading in bfloat16 with device_map='auto'...") |
|
|
| n_gpus = torch.cuda.device_count() |
| print(f" Detected {n_gpus} GPU(s)") |
|
|
| model = AutoModelForCausalLM.from_pretrained( |
| BASE_MODEL, |
| torch_dtype=torch.bfloat16, |
| device_map="auto", |
| trust_remote_code=True, |
| ) |
|
|
| if torch.cuda.is_available(): |
| allocated = torch.cuda.memory_allocated() / 1024**3 |
| print(f" Base model loaded, GPU memory: {allocated:.1f} GB") |
|
|
| |
| |
| |
| print(f"\n[3/5] Loading LoRA adapter: {ADAPTER_MODEL}") |
| model = PeftModel.from_pretrained(model, ADAPTER_MODEL) |
| print(" Adapter loaded successfully") |
|
|
| |
| |
| |
| print("\n[4/5] Merging adapter into base model...") |
| model = model.merge_and_unload() |
| print(" Merge complete!") |
|
|
| if torch.cuda.is_available(): |
| allocated = torch.cuda.memory_allocated() / 1024**3 |
| print(f" GPU memory after merge: {allocated:.1f} GB") |
|
|
| |
| |
| |
| print(f"\n[5/5] Pushing merged model to Hub: {MERGED_MODEL_ID}") |
| print(" Pushing model (this may take a while for large models)...") |
| model.push_to_hub(MERGED_MODEL_ID) |
| print(" Model pushed!") |
|
|
| print(" Pushing tokenizer...") |
| tokenizer.push_to_hub(MERGED_MODEL_ID) |
| print(" Tokenizer pushed!") |
|
|
| print("\n" + "=" * 60) |
| print(" MERGE COMPLETE!") |
| print("=" * 60) |
| print(f"Merged model: https://huggingface.co/{MERGED_MODEL_ID}") |
|
|
|
|
| if __name__ == "__main__": |
| exit(main() or 0) |
|
|
