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AI 모델 84759

• YOLO11↗

  Ultralytics

  Find detailed documentation in the Ultralytics Docs. Get support via GitHub Issues. Join discussions on Discord, Reddit, and the Ultralytics Community Forums! Request an Enterprise License for commercial use at Ultralytics Licensing. See below for quickstart installation and usage examples. For comprehensive guidance on training, validation, prediction, and deployment, refer to our full Ultralytics Docs.

• gemma-3-27b-it↗

  unsloth

  See our collection for all versions of Gemma 3 including GGUF, 4-bit & 16-bit formats. - Fine-tune Gemma 3 (12B) for free using our Google Colab notebook here! - Read our Blog about Gemma 3 support: unsloth.ai/blog/gemma3 - View the rest of our notebooks in our docs here. - Export your fine-tuned model to GGUF, Ollama, llama.cpp or 🤗HF. * [Gemma 3 Technical Report][g3-tech-report] * [Responsible Generative AI Toolkit][rai-toolkit] * [Gemma on Kaggle][kaggle-gemma] * [Gemma on Vertex Model Garden][vertex-mg-gemma3]

• Mistral-Medium-3.5-128B-GGUF↗

  unsloth

  We worked with Mistral to fix Mistral Medium 3.5 inference issues affecting some implementations (**not related to Unsloth** or our quants). The issue came from a YaRN parsing quirk in implementations like transformers and llama.cpp. Setting `mscale_all_dim` from `1` to `0` fixes it, including the model forgetting previous conversations. Mistral has now pushed these fixes to their official repo. Mistral Medium 3.5 is our first flagship merged model. It is a dense 128B model with a 256k context window, handling instruction-following, reasoning, and coding in a single set of weights. Mistral Medium 3.5 replaces its predecessor Mistral Medium 3.1 and Magistral in Le Chat. It also replaces Devstral 2 in our coding agent Vibe. Concretely, expect better performance for instruct, reasoning and coding tasks in a new unified model in comparison with our previous released models. Reasoning effort is configurable per request, so the same model can answer a quick chat reply or work through a complex agentic run. We trained the vision encoder from scratch to handle variable image sizes and aspect ratios.

• SaProt_650M_PDB↗

  westlake-repl

• Dark-Scarlett-v1.0-QAT-26B-A4B-Voice↗

  Wiself

• vjev-vision↗

  yah01

• CodeV-CL-7B↗

  yang-z

• Youtu-LLM-2B↗

  腾讯

  **Youtu-LLM** is a new, small, yet powerful LLM, contains only 1.96B parameters, supports 128k long context, and has native agentic talents. On general evaluations, Youtu-LLM significantly outperforms SOTA LLMs of similar size in terms of Commonsense, STEM, Coding and Long Context capabilities; in agent-related testing, Youtu-LLM surpasses larger-sized leaders and is truly capable of completing multiple end2end agent tasks. **Youtu-LLM** has the following features: - Type: Autoregressive Causal Language Models with Dense MLA - Release versions: Base and Instruct - Number of Parameters: 1.96B - Number of Layers: 32 - Number of Attention Heads (MLA): 16 for Q/K/V - MLA Rank: 1,536 for Q, 512 for K/V - MLA Dim: 128 for QK Nope, 64 for QK Rope, and 128 for V - Context Length: 131,072 - Vocabulary Size: 128,256 > **Note:** The GGUF weights of Youtu-LLM-2B are available on the `GGUF` branch of this repository. Please specify the revision (`GGUF`) when downloading them:

• GLM-5.2-FP8↗

  zai-org

  We're introducing GLM-5.2, our latest flagship model for long-horizon tasks. It marks a substantial leap in long-horizon task capability over its predecessor GLM-5.1 and, for the first time, delivers that capability on a **solid 1M-token context**. GLM-5.2's new capabilities include: - **Solid 1M Context:** A solid 1M-token context that stably sustains long-horizon work - **Advanced Coding with Flexible Effort**: Stronger coding capabilities with multiple thinking effort levels to balance performance and latency - **Improved Architecture**: We propose IndexShare, which reuses the same indexer across every four sparse attention layers, reducing per-token FLOPs by 2.9× at a 1M context length. We also improve GLM-5.2’s MTP layer for speculative decoding, increasing the acceptance length by up to 20% - **Pure Open**: An MIT open-source license — no regional limits, technical access without borders GLM-5.2 supports deployment with the following frameworks. Feel free to try them out: - SGLang (v0.5.13.post1+) — see cookbook - vLLM (v0.23.0+) — see recipes - Transformers (v0.5.12+) — see transformers docs - KTransformers (v0.5.12+) — see tutorial - Unsloth (v0.1.47-beta+) — see guide - For deployment on the `Ascend NPU` platform, inference frameworks such as vLLM-Ascend, xLLM and SGLang are supported — see here.

• miqu-1-70b-sf↗

  152334H