HuggingFace Integration

Hugging Face is a leading platform for building, hosting, and deploying state-of-the-art machine learning models. With its powerful Transformers library and public model hub, Hugging Face makes it easy to access and run pre-trained models across tasks like text classification, generation, summarization, and more. You can now bring custom or fine-tuned HuggingFace models directly into Lamatic Studio. This allows full control over AI workflows, logic, and model performance — tailored to your specific use case.

You can now integrate HuggingFace models directly into Lamatic.ai Studio (opens in a new tab), whether they’re publicly available or hosted privately using dedicated inference endpoints.

Setup Instructions

Using HuggingFace Dedicated Inference Endpoints

Use this method for private or fine-tuned models with higher performance and security.

1. Go to the Hugging Face Hub (opens in a new tab) and log in.
2. Navigate to your Inference Endpoints: https://endpoints.huggingface.co/your-username/endpoints/dedicated
3. Copy your Base URL.
4. Visit Hugging Face Access Tokens (opens in a new tab) and generate a new token.
5. Open Lamatic.ai Studio (opens in a new tab).
6. Go to Models → Huggingface.
7. Paste your Base URL and Access Token.
8. Save the configuration.

Using HuggingFace Public Models

Best for quick testing or integrating popular open-source models.

1. Go to Hugging Face Hub (opens in a new tab) and find your desired public model.
2. Use the public inference URL format: https://api-inference.huggingface.co/models/your-model-name
3. Generate a [Hugging Face Access Token] if required.
4. In Lamatic.ai Studio (opens in a new tab):

• Navigate to Models → Huggingface
• Paste the model URL and token
• Save your settings

Key Benefits

• Customizability
  Use internal or domain-specific fine-tuned models to match your needs.

• Model Flexibility
  Access public models or host private ones using HuggingFace endpoints.

• Seamless Integration
  Easily connect HuggingFace to Lamatic workflows with no additional infrastructure setup.

• Credential Support
  Authenticate using custom tokens and manage access securely.

• Provider-Level Customization
  Override default behavior on a per-provider basis.

Check out the full Custom Model Integration Docs (opens in a new tab) for more advanced configuration options.

Best Practices

• Keep your API keys and tokens private.
• Rotate your credentials periodically.
• Test model responses after integration.
• Use dedicated endpoints for production-grade use cases.
• Monitor rate limits and billing on Hugging Face if using large models.