Where's my google meet link @Thom_Wolf ? v/ @ClementDelangue @huggingface CNBC Link https://
cnbc.com/2023/11/22/in-
openai-fallout-open-source-ai-could-be-among-the-big-tech-winners.html
… #GenAI #chatgpt #llm #gpt4 #OenAI #microsoft #promptengineering #GenerativeAI #LLAMA #ai #gpt4 #chatgpt #stats #statistics #DataScience
LLMS
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Open-source AI emerges as big tech winner amid OpenAI changes
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AI Evolution: From ELMo to GPT-3 and Beyond 500B Parameters
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Tracing the AI explosion from ELMo to GPT-3 and beyond, thanks to Aritra Ghosh's timeline. The future is here with models reaching 500+ billion parameters!
Stay ahead in #AI with @ingliguori and his ebook The Digital Edge. Get your ebook https://
bit.ly/3u4pILl #TechTrends -
AI cycle early stage: Mistral, Meta, Grok catching up fast
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C'est absolument pas trop tard! Le cycle technologique est encore au tout début et tout le monde peut rattraper vite (ex @MistralAI @AIatMeta @grok …)
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Large Language Models Explained Simply Without Complex Math
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Large language models, explained with a minimum of math and jargon https://
bit.ly/3OMcESV #AI #MachineLearning #DeepLearning #LLMs #DataScience -
SambaStudio Adds Text Embedding Models RAG Capabilities
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TECH BLOG: SambaStudio now supports text embedding models, enhancing #LLMs with Retrieval Augmented Generation (RAG) capabilities. RAG improves base LLMs on factuality, reduced hallucinations, and more by providing correct context to the LLM. https://
sambanova.ai/blog/introduci
ng-text-embedding-model-support-in-sambastudio-elevating-information-retrieval-and-augmenting-large-language-models/
… #ai #ml -
MLflow 2.8 LLM-as-a-Judge Evaluation for RAG Applications
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Get out-of-the-box metrics like latency, tokens and more, using #MLflow 2.8 with LLM-as-a-judge Discover how it can save you time and money and best practices for #LLM evaluation in RAG applications https://
bit.ly/3FHCWAg -
TheBloke gains significant followers on Hugging Face platform
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So cool to see that @TheBlokeAI has almost as many follwrs on HF than here! https://
huggingface.co/TheBloke -
LangSmith Benchmarks: Evaluate LLM Performance on Your System
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More to come! Check out the blog post for more information on how to get started, or see the benchmark docs to run these on your own system. https://
blog.langchain.dev/public-langsmi
th-benchmarks/
… https://
langchain-ai.github.io/langchain-benc
hmarks/index.html
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LangChain Benchmarks Package for LLM and Embedding Comparison
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Try it yourself We've published the new langchain-benchmarks package, which provides tooling to easily compare LLMs, embeddings, indexing techniques, and more across these datasets, so you can find the optimal solution for each task.
https://
langchain-ai.github.io/langchain-benc
hmarks/notebooks/retrieval/langchain_docs_qa.html
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LangChain Playground: Test LLM Improvements in Browser
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If you’re logged in, you can even use the playground to try out different improvements in the browser (by clicking on any LLM run). https://
smith.langchain.com/public/452ccaf
c-18e1-4314-885b-edd735f17b9d/d/80501c49-6845-4a1e-980d-8e36dddba230/p/r/965f74aa-6c87-490c-98aa-158db8358fe9/playground
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