サイト内検索
AI モデル 84757
- occiglot-7b-it-en-instruct↗
occiglot
- realvisxl-v5.0-GGUF↗
off-grid-ai
- tiny-random-jais↗
onnx-community
- DeepSeek-V4-Flash-0731-JANG↗
OsaurusAI
- Qwen3.6-35B-A3B-MXFP4↗
pahajokiconsulting
- Laguna-M.1↗
poolside
Laguna M.1 is a 225B total parameter Mixture-of-Experts model with 23B activated parameters per token designed for agentic coding and long-horizon work. > [!NOTE] > For more details on how we trained this model, including our Model Factory approach, post-training recipe, async off-policy agent RL, and evaluations, check out our release blog post and technical report. * **Large sparse MoE for agentic coding**: Laguna M.1 is a 70-layer MoE transformer with 225B total parameters and 23B activated parameters per token * **High-capacity expert routing**: After 3 dense SwiGLU layers, Laguna M.1 uses 67 sparse MoE layers with 256 experts, top-k=16 routing and auxiliary-loss-free load balancing * **Global attention architecture**: Laguna M.1 uses global attention across all layers with 64 Q-heads, 8 KV-heads and softplus attention output gating * **Native reasoning support**: Interleaved thinking between tool calls with support for enabling and disabling thinking per-request * **Strong agentic benchmark performance**: Laguna M.1 is competitive with state-of-the-art open-weight and frontier models on SWE-bench Verified, SWE-bench Multilingual, SWE-Bench Pro and Terminal-Bench 2.0 * **Apache 2.0 license**: Use and modify freely for commercial and non-commercial purposes
- Ternary-Bonsai-2-9B-mlx-2bit↗
prism-ml
> **PrismML-Eng/Bonsai-demo is the source of truth for running these models.** Where anything here disagrees with it, it is right. For an OpenAI-compatible server, run `python -m mlx_vlm.server --model bonsai2-9b-mlx` and send the sampling settings in each request. For CUDA, CPU, and llama.cpp on Metal, use the GGUF packs of the same weights: **Ternary-Bonsai-2-9B-gguf**.
- TAIDE-LX-7B-GGUF↗
QuantFactory
- Hermes-2-Pro-Mistral-7B-iMat-GGUF↗
qwp4w3hyb
- gemma4-26b-claude-coder-GGUF↗
rafw007