Lightning-AI / Lightning-AI/litgpt
Fine-Tuning Chat Model with Domain-Specific Data for custom dataset
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- Dominant language
- Python
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Description
I have domain-specific data that I've preprocessed from PDFs into a c4_demo.json format, where each entry looks like [{"text": "<data from file1.pdf"}]. Now, I want to fine-tune my model for chat-based interactions to generate structured chat outputs. My dataset is currently in the following format: [{"role": "system", "content": ""}, {"role": "user", "content": ""}, {"role": "assistant", "content": ""}, ...]. Which fine-tuning method should I use for this?
Contributor guide
First steps
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- Open a pull request that references the issue number.
Research direction
The issue mentions c4_demo.json and a role/content dataset, but no source file, test, or entry point. Start by reviewing LitGPT’s existing fine-tuning recipes for custom datasets and comparing the two formats shown; done means identifying a supported path for structured chat outputs.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- machine-learning, python
- Domain
- ai, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 15/100