Thanks to @abdiisan for implementing an OpenRouter version of `ai-oracle`! https://
x.com/abdiisan/statu
/abdiisan/status/1777423304086753725
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LLMS
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OpenRouter Implementation of AI-Oracle Tool Released
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Flan-2 Published in JMLR: Scaling Instruction Tuning
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Flan-2 is published in JMLR https://
jmlr.org/papers/v25/23-
0870.html
…. I think it's a nice piece of history. The work scaled instruction tuning with respect to model size and finetuning tasks, which both improved performance. Our MMLU was 75%, SOTA when the paper came out in Oct 2022. Our -
GenAI Use Cases Discussed at RAISE Summit Event
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#Clap de fin du #RAISESummit Ravie d’avoir pu croiser @ProustNicolas @Oracle pour parler cas d’usage sur la #GENAI. More to come ! #stayTuned #IA #IntelligenceArtificielle #LLM #ML #innovation #Tech
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Serverless ChatGPT RAG with LangChain.js and Azure
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🚀Serverless ChatGPT with RAG using LangChain.js
— LangChain (@LangChain) 8 avril 2024
Great OSS repo that shows how to build a serverless ChatGPT-like experience with Retrieval-Augmented Generation using LangChain.js and Azurehttps://t.co/Bh95rgu7Bb pic.twitter.com/UlLf38DKrbServerless ChatGPT with RAG using LangChain.js Great OSS repo that shows how to build a serverless ChatGPT-like experience with Retrieval-Augmented Generation using LangChain.js and Azure https://
github.com/Azure-Samples/
serverless-chat-langchainjs?tab=readme-ov-file
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Combining Multiple AI Models for Improved Results
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It's not perfect, but on average, it should improve results significantly compared to using models individually. If anyone wants to improve it, there a lot of gains to be made by adding context about the strengths/weaknesses of each model in the final prompt.
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Multi-Model AI Orchestration Combining Claude GPT-4 Perplexity
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How does it work? The process is super simple. We simply query each model individually:
– Claude 3 Opus for reasoning + personality
– GPT-4 for reasoning
– PPLX for freshness/up-to-date info Then, Claude combines the strengths of each and responds with a final, ideal output. -
Top Data Scientists Master LLM Task Optimization with AI Co-pilots
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The top data scientists excel at getting LLMs to fulfill a specific task. Take, for instance, the Abacus AI co-pilot, which is capable of data wrangling, EDA, algorithm exploration and building a production-ready model in a couple of hours! Data science has now evolved into a
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Fine-tuning Models: When to Use It Versus Prompting
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If you haven’t hit PMF, you should not be fine-tuning models. Just prompt. Only exceptions:
– Speed is required for your use-case (and you need a model more powerful than Mixtral)
– Opus/GPT-4 cannot do your task or are too expensive per use -
How attention works in LLMs simplified
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How attention mechanisms work in LLMs, simplified: pic.twitter.com/9uMgauu7lk
— God of Prompt (@godofprompt) 8 avril 2024How attention mechanisms work in LLMs, simplified:
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ChatGPT mega-prompt for expert business problem-solver coach
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Here's a ChatGPT mega-prompt that turns it into an expert business problem-solver: #CONTEXT:
You are an expert Business Coach AI. You are a world-class coach for Entrepreneurs who build their small businesses with limited resources. You are well-known for helping people to find