Key steps to fine-tune LLM with zero code
Kiln tools are designed as complete visualization solutions for non-technical users:
- environmental preparation: First make sure to download the Kiln desktop for the corresponding operating system (.exe for Windows, .dmg for MacOS)
- Model Selection: After launching the app, you can select Llama/GPT4o/Mixtral preset models directly in the "Tweak" module.
- data entry: Two ways are supported:
- Upload existing training data in JSON format
- Use the built-in Synthetic Data Builder to create data by drag and drop.
- Parameter Configuration: Provides a visual tuning panel with recommended values for key parameters such as epochs/batch_size.
- One-Click Deployment: API endpoints are automatically generated after fine-tuning is complete and can be called directly via REST
Advanced Tip: In the "Tip Generation" module, you can automatically create a few-shot tip template, which greatly improves the fine-tuning effect.
This answer comes from the articleKiln: Simple LLM model fine-tuning and data synthesis tool, 0 code base to fine-tune your own small modelsThe
May not be reproduced without permission:AI productivity tools " How to achieve zero-code base for fine-tuning large language models like Llama, GPT4o, etc.?
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