SambaNova @huggingface Don't miss an #OpenSource #AI night to remember! Hosted by: @MurrayNewlands @_akhaliq Connect with AI experts from all over Silicon Valley and VCs who are at the cutting edge. Learn more about the event https://
lu.ma/ubkc4glk
OPEN SOURCE
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SambaNova Open Source AI Night Event in Silicon Valley
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Using Claude to Learn MCP from Documentation
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Feed all the docs into Claude as a plain text file and have Claude teach you about MCP: https://
modelcontextprotocol.io/llms-full.txt -
New Open-Source Python Package Simplifies LLM Integration
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Announcing new open-source Python package: aisuite! This makes it easy for developers to use large language models from multiple providers. When building applications I found it a hassle to integrate with multiple providers. Aisuite lets you pick a "provider:model" just by
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MCP: Open Standard for LLM Integrations Like LSP for IDEs
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Like LSP did for IDEs, we're building MCP as an open standard for LLM integrations. Build your own servers, contribute to the protocol, and help shape the future of AI integrations:
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Qwen 2 VL + FLUX: Advanced Open Source Image Generation
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Qwen 2 VL + FLUX > supports variation, img2img, inpainting, and controlnet-guided generation
> depth estimation and line detection for precise structural guidance
> aspect ratios up to 1536×1024 MIT licensed! -
Hugging Face SmolLM: Lightweight Language Model Released
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What are ya waiting for? https://
github.com/huggingface/sm
ollm
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SmolLM: Open Source Language Model Training and Deployment
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SmolLM – run, pre-train, fine-tune, evaluate SoTA fully open source LM Run with Transformers, MLX, Transformers.js, MLC Web-LLM, Ollama, Candle and more! Apache 2.0 licensed codebase – go explore now!
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Share Your Code: Community Contributions Welcome
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Any code to share? Would love to see and share!
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Production tips for open and closed source AI
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a lot of alphas in this fresh unreleased slide-deck by @NielsRogge (well now it's released hahah – sorry Niels) production uses/tips for open and closed source AI https://
docs.google.com/presentation/d
/1LwiPxriVRulQtxsLPck2G8Ya_eMjx08lsXL7TVkRnKo/edit?usp=sharing
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Optimizing llama.cpp quantization performance gains
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good reminder: I need to check my llama.cpp quants I suspect I’m leaving perf on the table.