Mastering LLMs course notes – Lecture 1
Notes on Mastering LLMs Maven course
- https://maven.com/parlance-labs/fine-tuning
Lecture 1
Most common tools for fine-tuning:
- Axolotl, huggingface’s transformer apply_chat() method — formats your fine-tune examples into templates that the LLM knows how to use
- https://github.com/OpenAccess-AI-Collective/axolotl
- https://huggingface.co/docs/transformers/main/en/chat_templating
- Many issues in development are due to templating inconsistencies (e.g. the special token that you put between prompt & response in the fine-tuning examples)
- (May 2024 statement) With larger context windows, bigger LLMs are able to take in more examples in the prompt itself, which makes fine-tuning less important
- Is this true?
- You should prove to yourself that you need to fine-tune — and only do it so after you’ve tried using the base model, doing prompt eng, etc.
- A lot of reasons to fine-tune are...