Fine-tune your own Llama 2 to replace GPT-3.5/4

The article discusses the concept of fine-tuning open-source LLMs, a more powerful form of prompting where instructions are encoded in the model's weights. The author shares insights and practical code on fine-tuning models, including labeling data, running efficient inference, and evaluating costs/performance. The author highlights that fine-tuning is more effective at guiding a model's behavior than prompting, allowing for smaller models, faster responses, and lower inference costs. For instance, a fine-tuned Llama 7B model is 50x cheaper than GPT-3.5 on a per-token basis.

The author also mentions the work on an open-source product called OpenPipe, aimed at helping engineers adopt fine-tuning as simply as possible. However, the author clarifies that the information shared in the post is independent of their startup and is solely for sharing knowledge about fine-tuning. The author believes that fine-tuning can work with as few as 50 examples but recommends trying to get 1000+ if possible.

Key takeaways:

Fine-tuning is a powerful form of prompting that encodes instructions in the weights of the model itself, and it can be more effective at guiding a model's behavior than prompting.
Prompting has advantages over fine-tuning as it's easier and faster to iterate on instructions than label data and re-train a model.
A fine-tuned model can often be much smaller, leading to faster responses and lower inference costs. For example, a fine-tuned Llama 7B model is 50x cheaper than GPT-3.5 on a per-token basis.
The author and his brother are working on an open-source product called OpenPipe to help engineers adopt fine-tuning as simply as possible.

Fine-tune your own Llama 2 to replace GPT-3.5/4

Key takeaways:

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