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AI 모델 84759

• gpt-oss-triton-kernels↗

  kernels-community

  > [!CAUTION] > Starting from September 13, 2026, we will be removing the "model" type repositories of kernels (e.g., kernels-community/flash-attn3). Make sure you're using a latest version of kernels. If you face any disruption, please report them here: https://github.com/huggingface/kernels/issues/new. This is the repository card of kernels-community/gpt-oss-triton-kernels that has been pushed on the Hub. It was built to be used with the `kernels` library. This card was automatically generated.

• minor16↗

  kinggeezero

• min-dalle↗

  kuprel

• Muse-12B-GGUF↗

  LatitudeGames

• EXAONE-3.5-7.8B-Instruct-AWQ↗

  LGAI-EXAONE

  We introduce EXAONE 3.5, a collection of instruction-tuned bilingual (English and Korean) generative models ranging from 2.4B to 32B parameters, developed and released by LG AI Research. EXAONE 3.5 language models include: 1) **2.4B model** optimized for deployment on small or resource-constrained devices, 2) **7.8B model** matching the size of its predecessor but offering improved performance, and 3) **32B model** delivering powerful performance. All models support long-context processing of up to 32K tokens. Each model demonstrates state-of-the-art performance in real-world use cases and long-context understanding, while remaining competitive in general domains compared to recently released models of similar sizes. For more details, please refer to our technical report, blog and GitHub. This repository contains the AWQ-quantized weights of the instruction-tuned 7.8B language model with the following features:

• xiaobu-embedding-v2↗

  lier007

• LTX-2.5-22b-IC-LoRA-Pixel-Spatial-Upscaler↗

  Lightricks

• LFM3-VL-2B-FP8↗

  LiquidAI

• Nex-N2-mini-ultra-uncensored-heretic-GGUF↗

  llmfan46

• Qwen3.6-27B-A3B-Coder-MTP-GGUF↗

  ManniX-ITA

  A **code-specialist expert prune** of Qwen3.6-35B-A3B: the MoE is reduced from **256 experts to 184** (72 dropped per layer, ~35B→27B, still A3B active) using a **code-targeted competence map** (LiveCodeBench + MultiPL-E competence classes). Same router, attention, norms, MTP head and vision tower as the base — only the expert keep-set changes. **Served at top-10** (`num_experts_per_tok = 10`, baked as the default). This is a routing-recovery lever: after pruning to 184 experts, activating the top-10 (vs the base top-8) recovers instruction-following at **no cost to code** (see below). No fine-tuning, no distillation — pure expert selection + a routing-width dial. 1. **Competence map**: the 256e teacher is profiled per-expert on a balanced corpus + targeted **LiveCodeBench** and **MultiPL-E** (Rust/Java/JS) PASS-response classes. 2. **Drop map**: `wmax` aggregation with the LCB + MPE classes up-weighted (1.5) → 72/256 experts dropped per layer, protecting the code-competent experts. 3. **Top-10 routing** (`num_experts_per_tok = 10`) baked into the config → the shipped default. Pass `--override-kv qwen35moe.expert_used_count=int:8` to any llama.cpp tool to A/B back to native top-8.