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salma-remyx
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reacted to sergiopaniego's post with 🔥 about 17 hours ago
Frontier models use distillation as a step of their post-training pipelines. In 2026 it has three jobs: compress a big model into a small one, merge RL experts into a single model, and let a model teach itself. I wrote up which frontier models use each one and how: https://huggingface.co/blog/sergiopaniego/distillation-2026 It pairs with Class 2 of the Training an Agent series Ben and I are doing, where we teach these techniques hands-on with TRL!
reacted to satgeze's post with 🔥 1 day ago
First GGUF quants of Tencent's Hy3 (299B MoE), built before official llama.cpp support exists. Hy3 dropped ~30 hours ago with only MLX and MXFP4 quants, both datacenter-sized. So I converted it myself using a community llama.cpp fork that implements the hy_v3 architecture. What's in the repo: - IQ1_M (62GB, fits a 128GB MacBook), IQ2_M (90GB), Q2_K (101GB), all with 1M context baked in via YaRN - IQ quants are importance-matrix: bootstrap style. The static Q2_K ran RAM-resident to compute the imatrix, then IQ1_M and IQ2_M were requantized from the archived f16 with it - Fixed chat template (the stock one uses .format() calls llama.cpp's Jinja rejects) - Build instructions for the fork, including the two gotchas that cost me three build attempts Honesty section, because that is how these repos work: this is EXPERIMENTAL. Not needle-certified yet (1M is baked but unverified, certification ladder will be published either way). MTP layer exists in the checkpoint but no llama.cpp build can run hy_v3 MTP inference yet, so it is not included. Real gate outputs are on the card, misses and all, judge for yourself. https://huggingface.co/satgeze/Hy3-1M-GGUF Full quant ladder (Q3 through Q8) is mirroring to ModelScope for bigger hardware.
reacted to alexanderbering's post with 🔥 3 days ago
We just put our money where our manifesto is. Our position has been that the layer of an AI system that touches your data should be open and auditable, not something you rent on trust. So instead of asking you to believe that, we made it runnable. The ZenBrain Playground is a static HF Space that executes our open-source memory library, @zensation/algorithms (Apache-2.0, zero-dependency), live in your browser. No install, no backend, no mockup: it runs the actual published code. Four panels drive real functions from the library — Ebbinghaus-style retention curves, Hebbian strengthening/decay, sleep-consolidation replay + pruning, and Bayesian retrievability with confidence intervals. The library is vendored into the page, so what you run is the same code that's published on npm. ZenBrain is a neuroscience-inspired 7-layer memory architecture for AI agents. The open core is the 20-module algorithm library (FSRS, Hebbian, Ebbinghaus, sleep-consolidation, Bayesian confidence, and more). On LongMemEval-500 it reaches 91.3% of a long-context oracle's accuracy at 1/106 of the per-query token budget, and takes the highest mean rank across all 12 system-judge cells (4 systems x 3 LLM judges) against Letta, Mem0, and A-Mem. It ships with 11,589 automated tests. Full details are in the arXiv preprint. If you build agent memory, we'd genuinely like your eyes on it — open the Space, poke the algorithms, read the code, tell us where it breaks. Live demo: https://huggingface.co/spaces/zensation-ai/zenbrain-playground Model: https://huggingface.co/zensation-ai/zenbrain Paper: arXiv:2604.23878
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