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‎docs/hub/model-release-checklist.md‎

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# Model Release Checklist
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# Checklist for an impactful model release
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The Hugging Face Hub is the go-to platform for sharing machine learning models. A well-executed release can boost your model's visibility and impact. This section covers essential steps for a concise, informative, and user-friendly model release.
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### Prepare the Model
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- [ ] Finalize model weights and architecture
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- [ ] Set up a repository on Hugging Face Hub
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- [ ] Choose a license (e.g., Apache 2.0, MIT)
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## ⏳ Preparing Your Model for Release
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### Upload Model Files:
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- [ ] Add model weights, config files, and tokenizers (if applicable).
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### Creating a Comprehensive Model Card
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### Write a Model Card:
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- [ ] Add metadata (license, pipeline tag, datasets used, library etc.)
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- [ ] Include a link to the arXiv paper or blog post
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- [ ] Add a code snippet for loading and using the model
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A well-crafted model card (the ```README.md``` file in your repository) is essential for discoverability, reproducibility, and effective sharing. Your model card should include:
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### Link to Other Artifacts:
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- [ ] Connect the model to related datasets, demos, or papers on Hugging Face
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- [ ] Create a demo space for showcasing the model usage
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1. **Metadata Configuration**: The [metadata section](https://huggingface.co/docs/hub/model-cards#model-card-metadata) at the top of your model card (in YAML format) is crucial for discoverability and proper categorization. Be sure to include:
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```yaml
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---
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pipeline_tag: text-generation # Specify the task
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library_name: transformers # Specify the library
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language:
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- en # List language for your model
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license: apache-2.0 # Specify a license
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datasets:
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- username/dataset # List datasets used for training
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base_model: username/base-model # If applicable
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---
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```
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### Communicate:
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- [ ] Share the release on social media, blogs and on hf.co/posts
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- [ ] Engage with the community for feedback on the model discussions
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2. **Detailed Model Description**: Provide a clear explanation of what your model does, its architecture, and its intended use cases. This helps users quickly understand if your model fits their needs.
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3. **Usage Examples**: Provide clear, actionable code snippets that demonstrate how to use your model for inference, fine-tuning, or other common tasks. These examples should be ready to copy and run with minimal modifications.
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4. **Technical Specifications**: Include information about training parameters, hardware requirements, and any other technical details that would help users understand how to effectively use your model.
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5. **Performance Metrics**: Share comprehensive benchmarks and evaluation results. Include both quantitative metrics and qualitative examples to give users a complete picture of your model's capabilities and limitations.
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6. **Limitations and Biases**: Transparently document any known limitations, biases, or ethical considerations associated with your model. This helps users make informed decisions about whether and how to use your model.
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### Enhancing Model Discoverability and Usability
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To maximize your model's reach and usability:
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1. **Library Integration**: If possible, add support for one of the many [libraries integrated with the Hugging Face Hub](https://huggingface.co/docs/hub/models-libraries) (such as Transformers or Diffusers). This integration significantly increases your model's accessibility and provides users with familiar interfaces for working with your model.
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For example, to specify that your model works with the Transformers library:
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```yaml
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---
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library_name: transformers
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---
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```
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2. **Pipeline Tag Selection**: Choose the correct [pipeline tag](https://huggingface.co/docs/hub/model-cards#specifying-a-task-pipeline_tag) that accurately reflects your model's primary task. This tag determines how your model appears in search results and which widgets are displayed on your model page.
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Examples of common pipeline tags:
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- `text-generation` - For language models that generate text
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- `text-to-image` - For text-to-image generation models
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- `Image-Text-to-Text` - For vision language models that generate text
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- `Text-to-Speech` - For models that generate audio from text
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3. **Research Papers**: If your model originates from research papers, you can cite them in your model card and it will [link them automatically](https://huggingface.co/docs/hub/model-cards#linking-a-paper) in your model card. This provides academic context and allows users to dive deeper into the theoretical foundations of your work.
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```markdown
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## References
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* [Model Paper](https://arxiv.org/abs/xxxx.xxxxx)
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```
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4. **Model Collections**: If you're releasing multiple related models or variants, organize them into a [collection](https://huggingface.co/docs/hub/collections). Collections help users discover related models and understand the relationships between different versions or variants.
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5. **Demo application**: Create a [Hugging Face Space](https://huggingface.co/docs/hub/spaces) with an interactive demo of your model. This allows users to try your model directly without writing any code, significantly lowering the barrier to adoption. You can also [link the model](https://huggingface.co/docs/hub/spaces-config-reference) from the Space to make it appear on the model page dedicated UI.
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```markdown
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## Demo
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Try this model directly in your browser: [Space Demo](https://huggingface.co/spaces/username/model-demo)
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```
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6. **Quantized Versions**: Consider uploading quantized versions of your model (e.g., in GGUF or DDUF formats) to improve accessibility for users with limited computational resources. Link these versions using the [`base_model` metadata field](https://huggingface.co/docs/hub/model-cards#specifying-a-base-model) on the quantized model cards. You can also clearly document performance differences between the original and quantized versions.
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```yaml
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---
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base_model: username/original-model
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---
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```
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7. **Linking Datasets on the Model Page**: Link datasets in your ```README.md``` metadata to display those used directly on your model page.
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```yaml
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---
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datasets:
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- username/dataset
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- username/dataset-2
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---
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```
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8. **New Model Version**: If this model is an update of an existing one, you can specify it on the previous version model metadata. This will display a banner on the older model's page linking directly to this updated version.
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```yaml
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---
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new_version: username/updated-model
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---
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```
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9. **Visual Examples**: For image or video generation models, include examples directly on your model page using the [`<Gallery>` card component](https://huggingface.co/docs/hub/model-card-components). Visual examples provide immediate insight into your model's capabilities.
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```markdown
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<Gallery>
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![Example 1](./images/example1.png)
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![Example 2](./images/example2.png)
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</Gallery>
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```
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10. **Carbon Emissions**: Optionally specify the [carbon emissions](https://huggingface.co/docs/hub/model-cards-co2) associated with training your model. This information helps environmentally conscious users and organizations make informed decisions.
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```yaml
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---
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co2_eq_emissions:
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emissions: 123.45
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source: "CodeCarbon"
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training_type: "pre-training"
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geographical_location: "US-East"
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hardware_used: "8xA100 GPUs"
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---
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```
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### Access Control and Visibility
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1. **Visibility Settings**: Once everything is finalized and you're ready to share your model with the world, switch your model to public visibility in your [model settings](https://huggingface.co/docs/hub/repositories-settings). Before doing so, double-check all documentation and code examples to ensure they're accurate and complete
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2. **Gated Access**: If your model requires controlled access, use the [gated access feature](https://huggingface.co/docs/hub/models-gated) and clearly specify the conditions users must meet to gain access. This is particularly important for models with potential dual-use concerns or commercial restrictions.
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## 🏁 After Releasing Your Model
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A successful model release extends beyond the initial publication. To maximize impact and maintain quality:
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### Maintenance and Community Engagement
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1. **Verify Functionality**: After release, verify that all provided code snippets work correctly by testing them in a clean environment. This ensures users can successfully implement your model without frustration.
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For example, test your library code snippet:
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```python
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from transformers import pipeline
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# This should work without errors
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pipe = pipeline("text-generation", model="your-username/your-model")
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result = pipe("Your test prompt")
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```
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2. **Promote Your Work**: Share your model through relevant channels such as social media, research communities, etc. Remember that every model represents valuable work that deserves visibility.
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3. **Community Interaction**: Engage with users in the Community Tab by answering questions, addressing feedback, and resolving issues quickly. Clarify confusion, adopt useful suggestions, and close off-topic discussions or pull requests to keep the space focused.
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### Tracking Usage and Impact
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1. **Usage Metrics**: Monitor downloads and likes to track your model's popularity and adoption. You can access total download metrics in your model settings.
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2. **Monitor Contributions**: Regularly check your model tree to discover contributions made by the community. These contributions can provide valuable insights and potential collaboration opportunities.
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## Enterprise Features
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[Hugging Face Enterprise](https://huggingface.co/enterprise) subscription offers additional capabilities:
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1. **Access Control**: Set [resource groups](https://huggingface.co/docs/hub/organizations) to control access for specific teams or users, ensuring appropriate permissions across your organization.
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2. **Regional Compliance**: Select the data storage region (US/EU) for your model files to comply with regional data regulations and requirements.
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3. **Advanced Analytics**: Utilize [enterprise Analytics features](https://huggingface.co/docs/hub/enterprise) to gain deeper insights into usage patterns and adoption metrics.
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4. **Extended Storage**: Access additional private storage capacity to host more models and larger artifacts as your model portfolio grows.
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By following these comprehensive guidelines and examples, you'll ensure your model release on Hugging Face is clear, impactful, and valuable maximizing the value of your work for the AI community.

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