Generate comprehensive model cards and upload fine-tuned models to Hugging Face Hub with professional documentation
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references/GGUF_GUIDE.mdreferences/TROUBLESHOOTING.mdscripts/convert_gguf.pyCreate model cards and upload fine-tuned models to Hugging Face Hub.
If coming from training manager, you should have:
model_path, base_model, dataset, techniquetraining_config (LoRA rank, LR, epochs)final_loss, training_time, hardwareIf missing, ask for essential information.
Ask for:
username/model-nameOptions:
If GGUF selected, ask which levels. See references/GGUF_GUIDE.md.
| Method | Size | Quality |
|---|---|---|
| Q4_K_M | ~4GB | Good (Recommended) |
| Q5_K_M | ~5GB | Better |
| Q8_0 | ~8GB | Best |
Create README.md with:
from huggingface_hub import create_repo
create_repo("username/model-name", private=False, exist_ok=True)
from huggingface_hub import HfApi
api = HfApi()
# LoRA adapter
api.upload_folder(folder_path="./outputs/lora_adapter", repo_id="username/model")
# Model card
api.upload_file(path_or_fileobj="README.md", path_in_repo="README.md", repo_id="username/model")
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained("./outputs/lora_adapter")
model.save_pretrained_gguf("./gguf", tokenizer, quantization_method="q4_k_m")
Use scripts/convert_gguf.py for multiple quantizations.
from huggingface_hub import list_repo_files
print(list_repo_files("username/model"))
Upload Complete!
Model: https://huggingface.co/{repo_name}
Uploaded:
- LoRA adapter
- Model card
- GGUF files (if selected)
Next steps:
- Verify model page
- Add example outputs
- Run benchmarks
- Share on social media
| Error | Resolution |
|---|---|
| Repo exists | Use exist_ok=True |
| Permission denied | Check HF token has write access |
| Upload timeout | Use chunked upload |