Deploying Your Fine-Tuned Model with LLM Studio
- Generative AI
- H2O Cascade
This final lesson in the LLM course walks through exporting a fine-tuned model from H2O LLM Studio to the Hugging Face Hub, including generating a Hugging Face API key with write access and pushing a checkpoint from the View Experiments screen. It then recaps the course: why fine-tuning matters, how H2O LLM Studio removes the need for coding, the role of synthetic datasets, how to choose an LLM backbone, and optimization techniques like quantization and LoRA. The lesson closes by pointing ahead to the open source h2oGPT tool.
Key takeaways
- How to export a fine-tuned model from H2O LLM Studio to the Hugging Face Hub using the built in one click export
- What is needed before sharing a model on Hugging Face, an API key with write access
- The steps to push a checkpoint, from View Experiments to entering the Hugging Face account name and API key
- Why fine-tuning matters for specialization, data efficiency, faster development, and cost savings
- How H2O LLM Studio removes the need for coding while still giving real time performance insights
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