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AI Models 85003

• resnet152.tv_in1k↗

  timm

• seresnextaa101d_32x8d.ah_in1k↗

  timm

• sam3↗

  tiny-random

• Jeesup-svd-safety-l3_swift_remove30_swapgapiter_evfront_b010-GGUF↗

  tinyopsec

• 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