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Models2026-08-30 · source-backed
The AngelSlim/Hy4-preview-GGUF repo offers Q4_K_M at 435.20 GiB (4.86 bpw) and STQ1_0 at 213.66 GiB (2.38 bpw), benchmarked at 204.56 t/s prefill and 20.47 t/s decode on 8x H20. STQ1_0 comes from llama.cpp PR #22836 and uses ternary weights with 3:4 forced sparsity at 1.3125 bpw on routed-expert gate/up projections across 29 layers, with IQ2_XXS at 2.0625 bpw on the other 48. An imatrix is mandatory. (Hugging Face) The r/LocalLLaMA thread (821 upvotes) circulated a ~98% performance retention claim, and the top skeptical reply notes 98% on KL divergence alone can be misleading. I'd want task benchmarks before believing that number.
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78 layers where layer one is dense FFN and the other 77 are MoE, each with 256 routed experts and 1 shared expert, top-8 routing per token, plus a native 10B MTP layer (0.7B activated) built in for speculative decoding. FP8 and base variants released together on August 28; the...
DeepSeek released V4 Preview on April 24 with two open-weight variants: V4-Pro (1.6T total parameters, 49B activated via MoE) and V4-Flash (284B parameters, 13B activated). Both support 1M-token context windows. Both are Apache 2.0 licensed. Both are live right now on Hugging...
On July 14, llama.cpp merged native support for Tencent's Hunyuan Hy3 architecture (PR #25395), a 295B-parameter, 21B-active MoE. Any recent master build can load it now. Community GGUF quants (Q2_K, IQ2_M, Q4_K_M) from AngelSlim and others already ship on Hugging Face, and so...
Tencent published UI-Mate-27B on Hugging Face, built on Qwen3.6-27B, reading live screenshots and emitting pyautogui-style tool calls. The paper (arXiv 2608.15930) reports 77.0 on OSWorld-Verified and 66.2 on WindowsAgentArena, beating its base by 17.7 and 24.5 points, and int...
For about a year, "run your agent locally" meant accepting a model that couldn't reliably call a tool twice in a row. That excuse is gone. Meta Superintelligence Labs published Muse Glimmer today: a 29.6B dense causal transformer, 52 layers, 6,656 hidden dim, with a ~1.8B ViT-...
Released August 28 with 78 layers, 77 of them MoE with 256 routed plus one shared expert and top-8 routing, plus a native 10B MTP layer for speculative decoding (GitHub). The attention stack uses Gated DeepSeek Sparse Attention with IndexCache for cross-layer sparse index reus...
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