Introducing Muse Glimmer, an open-weight 30B-parameter model optimized for local, always-on agent workflows.
Muse Glimmer delivers strong performance on key agentic use cases and benchmarks compared with leading models in its size category, and is designed to run entirely on consumer hardware like a Mac or PCs with performant GPUs.
In keeping with our long tradition of sharing fundamental AI research, we’re releasing model weights under a permissive Apache 2.0 license.
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We've open-sourced FlashKDA, our high-performance CUTLASS-based implementation of Kimi Delta Attention kernels.
It delivers 1.72×–2.22× prefill speedup over the flash-linear-attention baseline on H20, and works as a drop-in backend for flash-linear-attention.
Explore on GitHub:
https://t.co/QScjWsJqSy
The entire RAG industry is about to get cooked.
Researchers built a new RAG approach that skips almost everything traditional RAG relies on.
- No vector DB
- No embedding your data
- No chunking
- No similarity search
It's called PageIndex.
Instead of chopping your docs and stuffing them into Pinecone, it builds a tree index and lets the LLM reason through your documents like a human reading a book.
98.7% on FinanceBench. Beats every vector RAG on the leaderboard.
100% Free. Open Source.
Introducing Unsloth for AMD 🚀
You can now train & run LLMs on your AMD hardware
• We collaborated with AMD to enable you to train & run 500+ models on AMD GPUs
• Works on Windows, WSL, Linux
• Train Qwen, Gemma on 3GB VRAM
GitHub: https://t.co/2kXqhhvLsb
Works on Radeon, Instinct, Ryzen and data center GPUs with up to 2× faster with 70% less VRAM and no accuracy loss via our custom Triton kernels and math algorithms. We also support optimized ROCm builds for GGUF & Safetensors inference.
Unsloth is an open-source local UI for faster LLM training and inference, with tool-call healing, code execution, secure web search, remote APIs, and HTTPS deployment. Connect local models to Claude Code, Codex agents and run the latest Kimi, GLM, DeepSeek, Qwen3.6, and Gemma 4 models.
🔗Blog + Guide: https://t.co/U9LqyRjFdj