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AI 모델 84759
- LFM2.5-24B-A2B-GGUF↗
unsloth
LFM2.5-24B-A2B is a 24B-parameter reasoning model with only 2B active parameters, built for **fast, private agents on-device**. It is the largest and final model in the LFM2.5 family, which now spans two orders of magnitude: from LFM2.5-230M to LFM2.5-24B-A2B. We recommend using it for agents that need to reason, call tools, and run reliably on your device. It is not recommended for knowledge-heavy tasks on its own: pair it with retrieval or web search, which plays to its tool-calling strength. > [!TIP] > 💡 **Note**: Unlike LFM2-24B-A2B, LFM2.5-24B-A2B is a pure reasoning model that always thinks before it answers. It adds a ` ` tag directly in the chat template when starting an assistant answer.
- Qwen3.8-Flash-Next↗
unsloth
- QVikhr-3-8B-Instruction-GGUF↗
Vikhrmodels
- mmbert32k-feedback-detector-merged↗
vllm-sr
- MiniCPM-V-4.7-35B-A3B-APEX-GGUF↗
White-Wolf13
- Spark-X2.5-4B-GGUF↗
XHToken
Spark-X2.5 is a compact, general-purpose language model for conversation, writing, translation, reasoning, coding, tool use, and agentic workflows. It uses a hybrid attention architecture, supports a native context length of up to 1M tokens, and covers more than 200 languages. For its architecture, training methods, benchmark results, fine-tuning, and citation, see the Spark-X2.5-4B. > [!NOTE] > Native `spark2_5` support requires llama.cpp `b10828` or later. > [!NOTE] > Native `spark2_5` support requires Ollama `v0.34.1` or later.
- Qwen3.6-35B-A3B-GGUF↗
yangzikang
- qwen-plus-2025-11-05↗
阿里巴巴 · Qwen
- Nova 2.0 Pro Preview (Medium)↗
Amazon
- Ming-3.0-Audio-Tiny-GGUF↗
audio-cpp
GGUF conversion of inclusionAI/Ming-3.0-audio-tiny, pinned to revision `25b96e30be3739849bf76c0f48ac11db4a262fb3`. Upstream code: Ming-3.0-audio-flash. The single GGUF contains the audio encoder, KDA/MLA MoE Thinker, Talker, audio VAE, CAMPPlus speaker encoder, tokenizers, and configuration. It supports offline transcription, audio understanding, named voices, voice cloning, and spoken responses. Speech output is mono 44.1 kHz. Both quantized packages preserve the audio encoder, Thinker attention and MLP projections, Talker, VAE and speaker encoder. TTS-only memory usage therefore does not shrink with Thinker expert quantization.