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AI モデル 84757

• Yi-1.5-9B-Chat-16K-GGUF↗

  gaianet

• Clef-GGUF↗

  ggml-org

  This is a decision model, to be used via `/v1/systemone` API. Requires https://github.com/ggml-org/llama.cpp/pull/29831 > [!IMPORTANT] > This model is automatically converted using https://github.com/ggml-org/convert

• InternVL3-2B-Instruct-GGUF↗

  ggml-org

• lev-GGUF↗

  ggml-org

  This is a decision model, to be used via `/v1/systemone` API. See https://github.com/ggml-org/llama.cpp/pull/29818

• paligemma-3b-ft-textcaps-224-jax↗

  Google

• Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-MTP-GGUF↗

  Gustav0-Freind

• whisper-large-v3-gguf↗

  handy-computer

  GGUF conversions of openai/whisper-large-v3 for use with transcribe.cpp. Ported from upstream commit 06f233f, pinned 2026-04-25. Validated against the transformers reference at transcribe.cpp commit 0a26478 on 2026-09-13. OpenAI Whisper large-v3 — converted to GGUF for transcribe.cpp. Multilingual transcription, language detection, and speech translation (audio in any supported language → English text). v3 family adds Cantonese (yue) and uses a 128-bin mel input. Encoder-decoder transformer; 30-second windows with chunked long-form decoding.

• granite-20b-functioncalling↗

  ibm-granite

• K2-Horizon-32B-FP8↗

  IFM

• Ling-mini-2.0↗

  inclusionAI

  Today, we are excited to announce the open-sourcing of **Ling 2.0** — a family of MoE-based large language models that combine **SOTA performance** with **high efficiency**. The first released version, Ling-mini-2.0, is compact yet powerful. It has **16B total parameters**, but only **1.4B** are activated per input token (non-embedding 789M). Trained on more than **20T tokens** of high-quality data and enhanced through multi-stage supervised fine-tuning and reinforcement learning, Ling-mini-2.0 achieves remarkable improvements in complex reasoning and instruction following. With just 1.4B activated parameters, it still reaches the top-tier level of sub-10B dense LLMs and even matches or surpasses much larger MoE models. We evaluated Ling-mini-2.0 on challenging general reasoning tasks in coding (LiveCodeBench, CodeForces) and mathematics (AIME 2025, HMMT 2025), as well as knowledge-intensive reasoning tasks across multiple domains (MMLU-Pro, Humanity's Last Exam). Compared with sub-10B dense models (e.g., Qwen3-4B-instruct-2507, Qwen3-8B-nothinking) and larger-scale MoE models (Ernie-4.5-21B-A3B-PT, GPT-OSS-20B/low), Ling-mini-2.0 demonstrated outstanding overall reasoning capabilities. Guided by Ling Scaling Laws, Ling 2.0 adopts a **1/32 activation ratio** MoE architecture, with empirically optimized design choices in expert granularity, shared expert ratio, attention ratio, aux-loss free + sigmoid routing strategy, MTP loss, QK-Norm, half RoPE, and more. This enables small-activation MoE models to achieve over **7× equivalent dense performance**. In other words, **Ling-mini-2.0 with only 1.4B activated parameters (non-embedding 789M) can deliver performance equivalent to a 7–8B dense model**.