### General Questions

**Q: What is the difference between Model Repositories and Model Endpoints?**

A: Model Repositories are storage systems for model files, while Model Endpoints are running services that serve inference requests. Repositories store models; Endpoints use models from repositories to serve API requests.

**Q: Can I use Model Repositories without Model Endpoints?**

A: Yes! You can use Model Repositories independently for storage, versioning, and sharing models. They're also useful with Instances.

**Q: What file formats are supported?**

A: Model Repositories support any file format. There's no restriction - you can store PyTorch models, TensorFlow models, ONNX, TensorRT engines, or any custom format.

<!-- **Q: Is there a size limit for models?**

A: EOS supports large objects. Individual files can be several GBs, and total repository size is limited only by your storage quota. -->

### Storage Questions

**Q: What storage options are available?**

A: Three options:
1. **New EOS Bucket** - TIR provisions a new bucket automatically
2. **Existing EOS Bucket** - Link to an existing EOS bucket
3. **External EOS Bucket** - Connect to external S3-compatible storage
<br></br>
**Q: Can I change the storage type after creation?**

A: No, storage type cannot be changed after creation. You'll need to create a new repository with the desired storage type.

**Q: How do I access files in a Model Repository?**

A: Multiple ways:
- **Dashboard:** File Browser tab for visual browsing
- **CLI:** Minio CLI (`mc`) or `s3cmd`
- **SDK:** Python SDK or other language SDKs
- **API:** S3-compatible REST API

**Q: Can I encrypt my models?**

A: Yes! EOS encryption is available. Enable encryption when creating a new EOS bucket. 
> Encryption must be enabled at bucket creation.

### Versioning Questions

**Q: How do I version my models?**

A: Use folder structures:
- `/v1`, `/v2` for numbered versions
- `/production`, `/staging` for environments
- `/experiments/exp-001` for experimental versions

**Q: Can I delete specific versions?**

A: Yes, you can delete specific folders/files using CLI or SDK. Be careful - deletion is permanent.

**Q: How many versions should I keep?**

A: Best practice: Keep 2-3 previous versions for rollback. Archive or delete older versions to save storage costs.

### Integration Questions

**Q: How do I link a Model Repository to a Model Endpoint?**

A: You can link a repository in two ways.<br></br>
 - **From Model Repository:** Use the **Deploy Model** option in the repository table; you are taken to the Model Endpoint flow, where you select the framework (e.g., vLLM, SGLang, NVIDIA Triton) and link the repository to the endpoint. 
 <br></br>
 - **From Model Endpoints:** Create a Model Endpoint, select **Link with Model Repository**, choose your repository, and specify the model path (e.g., `/` or `/v1`). In both cases, the endpoint automatically downloads model files from the repository during startup.

<!-- ### Access & Permissions

**Q: Who can access my Model Repository?**

A: All team members with access to the project can view and use repositories. Access is managed at the project level.

**Q: Can I restrict access to specific team members?**

A: Currently, access is project-wide. For restricted access, create separate projects or use IAM roles for fine-grained control. -->


### Troubleshooting

**Q: Upload is slow. How can I speed it up?**

A: Try the following:
- Use parallel uploads: `mc cp --parallel`
- Ensure good network connectivity
- Compress files before upload
- Use SDK for programmatic uploads (may be faster)

**Q: Model Endpoint can't find my model files. What's wrong?**

A: Check:
1. Model path in endpoint configuration matches folder structure
2. Files are actually uploaded (verify via File Browser)
3. Bucket name is correct
4. Check endpoint logs for specific errors




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