A Simple Convergence Proof of Adam and Adagrad https://
bit.ly/3zwWvvZ
#AI #MachineLearning #DeepLearning #LLMs #DataScience
LLMS
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Simple Convergence Proof of Adam and Adagrad Optimizers
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Anthropic Claude Powers Data Analyst Tool Development
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Nice! We're also using @AnthropicAI behind the scenes (Claude is really good at understanding data!) — and have a version of data analyst as well.
— Jiquan Ngiam (@JiquanNgiam) 18 octobre 2024
You guys building cool stuff!https://t.co/GpIke8Om5KNice! We're also using @AnthropicAI behind the scenes (Claude is really good at understanding data!) — and have a version of data analyst as well. You guys building cool stuff!
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Chat LangChain Improved with New Cognitive Architecture
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Improved Chat LangChain We have a new and improved Chat LangChain experience! The new version has a new cognitive architecture (uses a research subagent) and slicker UI Open source as always Check it out https://
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Open-PerfectBlend: 1.42M Sample SFT Dataset Released
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Open-PerfectBlend I reproduced the SFT dataset described in Meta's PerfectBlend paper: 1.42M samples with chat, math, code, and instruction-following samples. Give it a try for your next fine-tune! Dataset: https://
huggingface.co/datasets/mlabo
nne/open-perfectblend
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Guide to Creating Custom LLM Chatbots
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Chatbots are the most common applications of LLMs by far. Here’s a helpful graphic showing how to create your custom LLM chatbot.
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How to re-rank your snippets in RAG with ColBERT, Rerankers
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𝗛𝗼𝘄 𝘁𝗼 𝗿𝗲-𝗿𝗮𝗻𝗸 𝘆𝗼𝘂𝗿 𝘀𝗻𝗶𝗽𝗽𝗲𝘁𝘀 𝗶𝗻 𝗥𝗔𝗚 ⇒ ColBERT, Rerankers, Cross-Encoders Let’s say you’re doing RAG, and in an effort to improve performance, you try to rerank a few possible source snippets by their relevancy to a query. How can you score
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Claude Artifacts performance and rendering capabilities
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B/c at this stage Claude Artefacts performs better due to the rendering capabilities pic.twitter.com/Dj0XNlJbyC
— 🚨 AI News | TestingCatalog (@testingcatalog) 18 octobre 2024B/c at this stage Claude Artefacts performs better due to the rendering capabilities
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Aligning Large Language Models with Human Values and Intentions
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align any modality large models (any-to-any models), including LLMs, VLMs, and others, with human intentions and values. More details about the definition and milestones of alignment for Large Models can be found in AI Alignmen
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PKU releases Align Anything framework for AI alignment
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PKU opensourced 「Align Anything」framework https://
github.com/PKU-Alignment/
align-anything
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Fine-tuning Leaderboard: Which SLM to choose for your use case?
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Which #slm should I fine-tune?
— Predibase by Rubrik (@predibase) 18 octobre 2024
How does it perform for my use case?
How much improvement can I get over hashtag #GPT4?
We get lot of questions like these and to help you answer them, we've created the Fine-tuning Leaderboard: https://t.co/fqCTI4thOY
We #finetuned 20+ models… pic.twitter.com/AQ2sLHWV5fWhich #slm should I fine-tune? How does it perform for my use case? How much improvement can I get over hashtag #GPT4? We get lot of questions like these and to help you answer them, we've created the Fine-tuning Leaderboard: https://
predibase.com/fine-tuning-le
aderboard
… We #finetuned 20+ models