Model Upload & Card Generator

Create model cards and upload fine-tuned models to Hugging Face Hub.

Gather Context

If coming from training manager, you should have:

• model_path, base_model, dataset, technique
• training_config (LoRA rank, LR, epochs)
• final_loss, training_time, hardware

If missing, ask for essential information.

Configuration

1. Repository Settings

Ask for:

• Repo name: username/model-name
• Visibility: Public or Private
• License: MIT, Apache 2.0, CC-BY-4.0, Llama 3 Community, etc.

2. Export Formats

Options:

1. LoRA adapter only (~50-200MB) – Users merge themselves
2. Merged 16-bit (15-140GB) – Ready to use
3. GGUF quantized (4-8GB) – For llama.cpp/Ollama
4. All of the above (Recommended)

3. GGUF Quantization

If GGUF selected, ask which levels. See references/GGUF_GUIDE.md.

Generate Model Card

Create README.md with:

1. YAML Metadata – license, tags, base_model, datasets
2. Model Description – Table with key attributes
3. Training Details – Hyperparameters, LoRA config, results
4. Usage Examples – Transformers, Unsloth, Ollama, llama.cpp
5. Intended Use – Primary use cases, out-of-scope
6. Limitations – Biases, known issues
7. Citation – BibTeX entry

Execute Upload

1. Create Repository

from huggingface_hub import create_repo
create_repo("username/model-name", private=False, exist_ok=True)

2. Upload Files

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")

3. Generate GGUF (if selected)

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.

4. Verify

from huggingface_hub import list_repo_files
print(list_repo_files("username/model"))

Final Report

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

Model Card Best Practices

1. Be specific about limitations
2. Include usage examples – copy-pasteable
3. Document training details
4. Credit sources – base model, dataset, tools
5. Use tables – easier to scan

Error Handling

Bundled Resources

• scripts/convert_gguf.py – GGUF conversion
• references/GGUF_GUIDE.md – GGUF details and Ollama setup
• references/TROUBLESHOOTING.md – Upload issues