Devs, check out the tutorial @mervinpraison
— Groq Inc (@GroqInc) 21 avril 2024
has made with Llama3 from @AIatMeta https://t.co/LgiwsYC9pA
Devs, check out the tutorial @mervinpraison has made with Llama3 from @AIatMeta
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Devs, check out the tutorial @mervinpraison
— Groq Inc (@GroqInc) 21 avril 2024
has made with Llama3 from @AIatMeta https://t.co/LgiwsYC9pA
Devs, check out the tutorial @mervinpraison has made with Llama3 from @AIatMeta

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Build a serverless AI chat with RAG using LangChain.js This shows how LangChain.js, Ollama with Mistral 7B model and Azure can be used together to build a serverless chatbot that can answer questions using a RAG pipeline https://
techcommunity.microsoft.com/t5/apps-on-azu
re-blog/build-a-serverless-ai-chat-with-rag-using-langchain-js/ba-p/4111041
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The biggest challenge IMO is helping people understand the hallucination problem and how unreliable these tools are – if you don't understand that it's much harder to put these things to good use
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We can also figure out how to get useful results out of inherently unreliable algorithms – it's definitely possible, I've been finding useful ways to put this stuff to work for a couple of years now
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The amount of non-deterministic behavior LLMs introduce makes me believe in techno-sorcery futures. Let's chant a verse, burn candles, perform data prayers and result purification rituals. Anything to get this endpoints perform predictable across different input
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It generates hypotheses autonomously and tests them autonomously

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I like the Grok @X auto-generated summary.
I previously wrote a more in-depth review at Ground Truths (see profile)
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We want to do a full GPT-2 repro, at channel size 1600 this is 2.1X higher C. And we'll want to ~max out batch dim to fit in memory too. So the "easy times" will be over soon.