Pinned
Run GLM 5.2 with only 192 GB ram 🫰
Introducing a REAP50-Q3 quant by Pipe Network.
Reduced 50% in size by keeping half of the most-salient experts per layer.
Download on @huggingface: huggingface.co/pipenetwork/GL…
Pipe Network
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- Anybody want MLX for the smaller 9B and 35B Ornith?Ornith 397B is now available on Apple silicon 🫰 Specialized for agentic coding, this model features a self-improving training framework. MLX quants by Pipe Network are live on @huggingface: huggingface.co/collections/pi…
00:00 - Ornith 397B is now available on Apple silicon 🫰 Specialized for agentic coding, this model features a self-improving training framework. MLX quants by Pipe Network are live on @huggingface: huggingface.co/collections/pi…00:00
- By age 30, everyone should have: 1. Dual monitors 2. Gym subscription 3. Back pain 4. @NVIDIAAI DGX Station running GLM 5.2
- What's your favorite local model? One reply wins $500 in credits and early access 👇
- Multiple flavors of GLM 5.2 are now live! Run the size that fits your hardware: • REAP50 - 381B params • REAP37 - 472B params • REAP25 - 562B params Full Pipe Network collection on @huggingface: huggingface.co/collections/pi…
- Automatic routing to the best model for the job. Who's building this? 😏
- What hardware do you wish to use GLM 5.2 on? Taking requests 👇Run GLM 5.2 with only 192 GB ram 🫰 Introducing a REAP50-Q3 quant by Pipe Network. Reduced 50% in size by keeping half of the most-salient experts per layer. Download on @huggingface: huggingface.co/pipenetwork/GL…
- Pipe Network reposted
Run GLM 5.2 with only 192 GB ram 🫰 Introducing a REAP50-Q3 quant by Pipe Network. Reduced 50% in size by keeping half of the most-salient experts per layer. Download on @huggingface: huggingface.co/pipenetwork/GL… - Replying to @pipenetworkQuantized to Q3_K_M (~169 GB), you can run it with llama.cpp on: - Mac Studio with 192GB+ unified memory - 2× RTX Pro 6000 - 6× RTX 5090 - 8× RTX 3090
- Fifty one near-lossless models available for download from Pipe. What are you building today?
