Experimentation with "internal" llm #505
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simplify LLM's job. Do not request Json output with a single key. Instead, make sure LLM don't output any extra information. By simplifying LLM's job, we're making sure its output can be parsed. I did a quick test with the Translate prompt. Adding instructions to output only translated text seems enough after a bunch of tests. I did a small prompt engineering, using ChatGPT and Claude to generate a proper system prompt … it works quite okay BUT there is room for improvement for sure. I'ven't searched yet OS prompts we could find in a prompt library. Perfect translation job seems to be a difficult job for a 8B model. Please note I haven't updated yet the other prompts, let's discuss it before. I ran my experiment with our internal LLM which is optimized for throughput, and not latency (there is a trade-off). I'll try fine tune few of its parameters to see if I can reduce its latency. For 880 tokens (based on chatgpt tokens counter online). It takes roughly 17s, vs ~40s for Albert CNRS 70B. For 180 tokens it takes roughly 3s. Without a proper UX (eg. a nicer loading animation, streaming tokens) it feels a decade. However, asking Chatgpt the same job take the same amount, from submitting the request to the last token being generated.
AntoLC
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Dec 16, 2024
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I think it is a good idea to do directly in markdown.
This prompts as well should be improved, they use the same methods:
https://github.com/numerique-gouv/impress/blob/8b60bc57e6c980dfc5eed8932a3cf64244b2079b/src/backend/core/services/ai_services.py#L13-L36
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Yes ofc, I mention it in my commit "Please note I haven't updated yet the other prompts, let's discuss it before."
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Please check my first comit. It's draft