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AI Models 84759

• mobilevit-small-finetuned↗

  jorgeduardo13

• jun3wan212-lora↗

  jude1903

• Qwen3.5-4B-uncensored-GGUF↗

  julien-c

• DeepSeek-V4-Flash-0731-oQ4e-mtp↗

  Jundot

  > [!NOTE] > **Updated 2026-10-29:** Re-quantized with the improved oQe quantization in oMLX 0.7.3. Please re-download if you have an earlier copy. This model was quantized using oQ (oMLX v0.7.3) mixed-precision quantization with improved oQe (jundot/omlx#4930). A mixed-precision MLX checkpoint of deepseek-ai/DeepSeek-V4-Flash-0731, the official release of DeepSeek-V4-Flash with enhanced agentic capabilities. It has the same structure as DeepSeek-V4-Flash-DSpark, with a speculative decoding module attached. It includes the language model and the DSpark MTP weights.

• tiny-random-qwen2vl↗

  katuni4ka

• sentence-bert-swedish-cased↗

  KBLab

  This is a sentence-transformers model: It maps Swedish sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. This model is a bilingual Swedish-English model trained according to instructions in the paper Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation and the documentation accompanying its companion python package. We have used the strongest available pretrained English Bi-Encoder (all-mpnet-base-v2) as a teacher model, and the pretrained Swedish KB-BERT as the student model. A more detailed description of the model can be found in an article we published on the KBLab blog here and for the updated model here. **Update**: We have released updated versions of the model since the initial release. The original model described in the blog post is **v1.0**. The current version is **v2.0**. The newer versions are trained on longer paragraphs, and have a longer max sequence length. **v2.0** is trained with a stronger teacher model and is the current default.

• Krea-2-LoRA-darkbrush↗

  krea

• PAW-27B-X3.1-GGUF↗

  lackonendes

  _If this model is useful to you, support the work by following @nawariokra on X, or buy me a coffee on Ko-fi._ Run `Qwen/Qwen3.8-27B` with good quality on a single RTX 3090 (24 GB VRAM) at full 262,144-token context — 129 tok/s generation, up to 1268 tok/s prompt processing. This is an update to our earlier PAW-27B-X3 release, with lower quality loss at the same file sizes. Two example scenes, generated in one shot from a text prompt (three.js/WebGL code, output by this model, rendered in a browser):

• Qwen3.8-Flash-Next-AWQ-g32↗

  leoncca

• gemma-4-E2B-it-GGUF↗

  lmstudio-community

  *👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord*. **Model creator**: google **Original model**: gemma-4-E2B-it **GGUF quantization**: provided by LM Studio team using `llama.cpp` release b10076 🙏 Special thanks to Georgi Gerganov and the whole team working on llama.cpp for making all of this possible.