But here's the challenge: finding an amazing model is just the beginning. What happens when you discover a model that's 90% perfect for your use case, but you need that extra 10% of customization? Traditional fine-tuning infrastructure is complex, expensive, and often requires significant DevOps expertise to set up and maintain.

This is exactly the gap that Together AI and Hugging Face are bridging today. We're announcing a powerful new capability that makes the entire Hugging Face Hub available for fine-tuning using Together AI's infrastructure. Now, any compatible LLM on the Hub, whether it's from Meta or an individual contributor, can be fine-tuned with the same ease and reliability you expect from Together's platform.🚀

Here's all it takes to start fine-tuning a HF model on the Together AI platform:

That's it! Your model will be trained on Together's infrastructure and can be deployed for inference, downloaded or even uploaded back to the Hub! For private repositories, simply add your HF token with hf_api_token="hf_xxxxxxxxxxxx".

As seen in the example above, when you fine-tune a Hugging Face model on Together AI, you actually specify two models:

Think of the base model as a "training template." It tells our system how to optimally allocate GPU resources, configure memory usage, set up the training pipeline, and prepare the model for inference. Your custom model should have a similar architecture, approximate size, and sequence length to the base model for optimal results.

7B-Instruct (which uses Llama architecture), you'd use togethercomputer/llama-2-7b-chat as your base model template, because they share the same underlying architecture.

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