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AI モデル 449
- Magistral-Small-2509-MLX-5bit↗
lmstudio-community
_👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord_. **Model creator**: mistralai **Original model**: Magistral-Small-2509 **MLX quantization**: provided by LM Studio team using mlx_vlm LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.
- nomic-embed-text-v2-moe-distilled-high-quality↗
cnmoro
This Model2Vec model was created by using Tokenlearn, with nomic-embed-text-v2-moe as a base. The evaluation in the model card, was executed using this model (distilled), not the original. The process to create this one, was not a simple model2vec distill, this involved generating embeddings for 23M triplets (msmarco) with the original model, then training the tokenlearn model on it, with the nomic model as a base.
- MistralRP-Noromaid-NSFW-Mistral-7B-GGUF↗
Ttimofeyka
This is a merge model of pre-trained language models created using mergekit. The following models were included in the merge: * Undi95/Mistral-RP-0.1-7B * MaziyarPanahi/NSFW_DPO_Noromaid-7b-Mistral-7B-Instruct-v0.1
- MN-12B-Mag-Mell-R1↗
inflatebot
NOTE for newer users: "R1" here means "Revision 1". This model predates DeepSeek's R1; DeepSeek inadvertently made using this versioning scheme very annoying! This is a merge of pre-trained language models created using mergekit. Mag Mell R1 was tested with Temp 1.25 and MinP 0.2. This was fairly stable up to 10K, but this might be too "hot". If issues with coherency occur, try *in*creasing MinP or *de*creasing Temperature.
- dpr-ctx_encoder-multiset-base↗
facebook
**Model Description:** Dense Passage Retrieval (DPR) is a set of tools and models for state-of-the-art open-domain Q&A research. `dpr-ctx_encoder-multiset-base` is the context encoder trained using the Natural Questions (NQ) dataset, TriviaQA, WebQuestions (WQ), and CuratedTREC (TREC). `dpr-ctx_encoder-multiset-base`, `dpr-question_encoder-multiset-base`, and `dpr-reader-multiset-base` can be used for the task of open-domain question answering. The model should not be used to intentionally create hostile or alienating environments for people. In addition, the set of DPR models was not trained to be factual or true representations of people or events, and therefore using the models to generate such content is out-of-scope for the abilities of this model.
- hubert-base-ls960↗
facebook
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for more in-detail explanation of how to fine-tune the model. Authors: Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed
- functiongemma-270m-it-GGUF↗
unsloth
> [!Note] > FunctionGemma is intended to be fine-tuned for your specific function-calling task, including multi-turn use cases. FunctionGemma is a lightweight, open model from Google, built as a foundation for creating your own specialized function calling models. FunctionGemma is not intended for use as a direct dialogue model, and is designed to be highly performant after further fine-tuning, as is typical of models this size. Built on the Gemma 3 270M model and with the same research and technology used to create the Gemini models, FunctionGemma has been trained specifically for function calling. The model has the same architecture as Gemma 3, but uses a different chat format. The model is well suited for text-only function calling. The uniquely small size makes it possible to deploy in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. Furthermore, akin to the base Gemma 270M, the model has been optimized to be extremely versatile, performant on a variety of hardware in single turn scenarios, but should be finetuned on single turn or multiturn task specific data to achieve best accuracy in specific domains. To demonstrate how specializing the 270M parameter model can achieve high performance on specific agentic workflows, we have highlighted two use cases in the Google AI Edge Gallery app. The following is a code example of how to use FunctionGemma to generate a function call from a JSON definition using the Hugging Face Transformers library.
- LTX_2.3_Clay_Mation_Style_LoRa↗
vrgamedevgirl84
This LoRA creates a handcrafted **claymation-style diorama** inspired by classic **stop-motion animation**. Subjects appear sculpted from soft clay, with **visible fingerprints**, **hand-shaped textures**, **layered clay details**, and **miniature set-style environments**. Characters, animals, and objects look physically modeled by hand, with **rounded forms**, **stylized proportions**, and **expressive features**. Scenes resemble miniature film sets built from sculpted clay materials, often paired with **warm cinematic lighting** and **shallow depth of field** to enhance the handmade look.
- bioclip↗
imageomics
BioCLIP is a foundation model for the tree of life, built using CLIP architecture as a vision model for general organismal biology. It is trained on TreeOfLife-10M, our specially-created dataset covering over 450K taxa--the most biologically diverse ML-ready dataset available to date. Through rigorous benchmarking on a diverse set of fine-grained biological classification tasks, BioCLIP consistently outperformed existing baselines by 16% to 17% absolute. Through intrinsic evaluation, we found that BioCLIP learned a hierarchical representation aligned to the tree of life, which demonstrates its potential for robust generalizability. **See the `examples/` directory for examples of how to use BioCLIP in zero-shot and few-shot settings.** BioCLIP is based on OpenAI's CLIP. We trained the model on TreeOfLife-10M from OpenAI's ViT-B/16 checkpoint, using OpenCLIP's code. BioCLIP is trained with the standard CLIP objective to imbue the model with an understanding, not just of different species, but of the hierarchical structure that relates species across the tree of life. In this way, BioCLIP offers potential to aid biologists in discovery of new and related creatures, since it does not see the 454K different taxa as distinct classes, but as part of an interconnected hierarchy.
- Mistral-Small-3.2-24B-Instruct-2506-awq-sym↗
jeffcookio
Created with `llm-compressor`'s latest changes, quantized on a GH200, works well for me with vLLM's `main` branch on my RTX 3090Ti as of 2025-07-01. Per https://vllm-dev.slack.com/archives/C07QP347J4D/p1751401629797809?thread_ts=1751399869.254259&cid=C07QP347J4D, there is currently no way to get tool calling with Mistral-HF formatted models. I've worked around this on a GitHub branch here: https://github.com/sjuxax/vllm/tree/Mistral3.1-rebase . It includes code to remap the weights from HF-Mistral to Mistral, allowing use of `MistralTokenizer`.