Mike Hall’s Post

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Technical leader - Software Engineer - AI, Cloud, APIs, Data

Technical report: Learn about fine tuning  Small cheaper to run models can do better than GPT-4 with a small amount of fine-tuning. "We find that 4-bit LoRA fine-tuned models outperform base models by 34 points and GPT-4 by 10 points on average" How to fine-tune? LoRa method is the most well known and can be done on any decent gaming PC for learning. Many YouTube tutorials can be found. Having your own fine-tunes gives your business something unique in the market, you build your dataset up over time, building that value. Paper: https://lnkd.in/gi6PqyYt

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Chris Barlow

Creative thinking in tech capability development

7mo

Very interesting Mike Hall , what has been your experience with fine tuning? I’ve read that fine tuning should not be used as a way to impart significant knowledge or memories to an LLM, and that RAG is better suited to this. That said, my understanding is that RAG running may get expensive running inference due to token count with larger data sets. What do you think is the a) most effective, and b) most efficient, method to inject a large body of knowledge, say, a textbook to an LLM for it to be an expert in this subject matter? I was thinking about potentially a hybrid approach where you fine tune an LLM but index the subject matter you trained on in prompt to help with retrieval.

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