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AI モデル 85003

• llava-v1.6-34b↗

  liuhaotian

  **Model type:** LLaVA is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. Base LLM: NousResearch/Nous-Hermes-2-Yi-34B **Where to send questions or comments about the model:** https://github.com/haotian-liu/LLaVA/issues **Primary intended uses:** The primary use of LLaVA is research on large multimodal models and chatbots.

• Llama-4-Scout-17B-16E-Instruct-GGUF↗

  lmstudio-community

• Qwen2.5-1.5B-Instruct-GGUF↗

  lmstudio-community

  *👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord*. **Model creator:** Qwen **Original model**: Qwen2.5-1.5B-Instruct **GGUF quantization:** provided by bartowski based on `llama.cpp` release b3772 Over 29 languages including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, and Arabic.

• SmolLM2-135M-Instruct-GGUF↗

  lmstudio-community

  *👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord*. **Model creator:** HuggingFaceTB **Original model**: SmolLM2-135M-Instruct **GGUF quantization:** provided by bartowski based on `llama.cpp` release b3991 🙏 Special thanks to Georgi Gerganov and the whole team working on llama.cpp for making all of this possible.

• Qwen3.8-9B-heretic-uncensored-NVFP4-GGUF↗

  luxuansang

• samantha-1.2-mistral-7b-Mistral-7B-Instruct-v0.2-slerp-GGUF↗

  MaziyarPanahi

• BiomedVLP-CXR-BERT-specialized↗

  Microsoft

  First, we pretrain **CXR-BERT-general** from a randomly initialized BERT model via Masked Language Modeling (MLM) on abstracts PubMed and clinical notes from the publicly-available MIMIC-III and MIMIC-CXR. In that regard, the general model is expected be applicable for research in clinical domains other than the chest radiology through domain specific fine-tuning. **CXR-BERT-specialized** is continually pretrained from CXR-BERT-general to further specialize in the chest X-ray domain. At the final stage, CXR-BERT is trained in a multi-modal contrastive learning framework, similar to the CLIP framework. The latent representation of [CLS] token is utilized to align text/image embeddings. **CXR-BERT-specialized** is jointly trained with a ResNet-50 image model in a multi-modal contrastive learning framework. Prior to multi-modal learning, the image model is pre-trained on the same set of images in MIMIC-CXR using SimCLR. The corresponding model definition and its loading functions can be accessed through our HI-ML-Multimodal GitHub repository. The joint image and text model, namely BioViL, can be used in phrase grounding applications as shown in this python notebook example. Additionally, please check the MS-CXR benchmark for a more systematic evaluation of joint image and text models in phrase grounding tasks.

• elem2design↗

  Microsoft

• gemma-3-27b-it-abliterated↗

  mlabonne

  This is an uncensored version of google/gemma-3-27b-it created with a new abliteration technique. See this article to know more about abliteration. I was playing with model weights and noticed that Gemma 3 was much more resilient to abliteration than other models like Qwen 2.5. I experimented with a few recipes to remove refusals while preserving most of the model capabilities. In the original technique, a refusal direction is computed by comparing the residual streams between target (harmful) and baseline (harmless) samples.

• Qwen3-Swallow-30B-A3B-RL-v0.2-gguf↗

  mmnga-o