Fine-tune Your Own GPT with LLM Studio
- Generative AI
- H2O Cascade
This lesson walks through a hands-on workshop for fine-tuning a large language model using H2O LLM Studio, a no-code, open-source tool. It covers importing the Open Assistant conversational dataset, selecting a smaller backbone model like GPT Neo, and configuring dataset, tokenizer, and LoRA settings before launching an experiment. It shows how to monitor training progress, chat with the finished model to test its behavior, and export the result directly to Hugging Face. The lesson sets up a hands-on assignment where learners repeat the process on their own dataset.
Key takeaways
- How to import a conversational dataset like Open Assistant and set validation and sampling sizes in LLM Studio
- What a backbone model is and how to pick a smaller one like GPT Neo for a quick fine-tuning run
- Why LoRA fine-tuning should stay enabled unless a large GPU budget is available
- How to chat with a freshly trained model to sanity check its responses before deployment
- The finished model can be exported directly to Hugging Face with its model card populated automatically
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