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AI モデル 450
- wav2vec2-large-xlsr-53-faroese-100h↗
carlosdanielhernandezmena
**Paper:** ASR Language Resources for Faroese The "wav2vec2-large-xlsr-53-faroese-100h" is an acoustic model suitable for Automatic Speech Recognition in Faroese. It is the result of fine-tuning the model "facebook/wav2vec2-large-xlsr-53" with 100 hours of Faroese data released by the Ravnur Project (https://maltokni.fo/en/) from the Faroe Islands. The specific dataset used to create the model is called "Ravnursson Faroese Speech and Transcripts" and it is available at http://hdl.handle.net/20.500.12537/276.
- LTX_2.3_Wild_West_Style_LoRa↗
vrgamedevgirl84
This LoRA creates a **cinematic realistic Wild West video** look. It is a grounded frontier-western style for **text-to-video generation**, not actual frames or footage from any movies, TV shows, games, or existing franchises. The workflow used to create the sample videos can be found HERE. Just swap out the LoRA and prompt. All sample videos are **text to video**. **NOT image to video.** Characters, environments, and motion read as grounded Wild West frontier footage. Scenes feature **weathered cowboys**, **outlaws**, **sheriffs**, **ranchers**, and **settlers**, with **period-authentic western clothing**, **worn leather gear**, **cowboy hats**, **dust-covered boots**, **wooden saloons**, **frontier towns**, **desert plains**, **barns**, **stables**, and **rugged natural landscapes**. The atmosphere is **dusty**, **sunbaked**, **dramatic**, and **cinematic**, with realistic textures and strong environmental storytelling. The result feels like a scene from a modern high-end western film with immersive frontier realism and grounded human tension.
- qwen3-4b-heretic↗
DreamFast
An abliterated version of Qwen 3 4B created using Heretic v1.2.0. This model has reduced refusals while maintaining model quality, making it suitable as an uncensored text encoder for image generation models like Z-Image and FLUX.2 Klein 4B. Available in five ComfyUI-native quantized formats (FP8, INT8, INT4, NVFP4, MXFP8), all produced with SVD-guided learned rounding for maximum fidelity. > **Quality:** All quantized variants use SVD-guided learned rounding (AdaRound via convert_to_quant), which optimizes each weight's rounding direction to minimize output reconstruction error — noticeably higher fidelity than naive round-to-nearest quantization. All variants load natively in ComfyUI 0.30.0+ (no plugins) via the `comfy_quant` metadata embedded in each file.
- wav2vec2-conformer-rel-pos-large↗
facebook
Wav2Vec2 Conformer with relative position embeddings, pretrained on 960 hours of Librispeech 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. The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.
- Huihui-Qwen3.8-27B-abliterated-GGUF↗
Kaktus4048
This is an uncensored version of Qwen/Qwen3.8-27B created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. The newly added **Huihui-Qwen3.8-27B-abliterated-Underdog-Saluki** series come from ConwayResearch/Underdog-Saluki-27B-1.0 and ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF. Only layers **22 to 52 (0-based indexing)** have been ablated, while the other layers remain unablated. It may come with a small disclaimer warning. Due to ConwayResearch/Underdog-Saluki-27B-1.0 not providing an imatrix file, we will attempt to perform ablation by mixing the GGUF weights of Swift-1.5 and ConwayResearch/Underdog-Saluki-27B-1.0.
- gemma-4-12B-it-litert-lm↗
litert-community
This model card provides the Gemma 4 12B model in LiteRT-LM format ready for deployment on macOS, Linux and Windows, as well as web in a more limited capacity. The current `gemma-4-12B-it.litertlm` file supports *text, vision and audio modalities* as well as *Multi-Token Prediction (MTP)* for accelerated speculative decoding and lower latency inference. Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. This particular Gemma 4 model is medium sized, so it is ideal for desktop use cases. By running this model on device, users can have private access to Generative AI without requiring an internet connection. These models are provided in the `.litertlm` format for use with the LiteRT-LM framework. LiteRT-LM is a specialized orchestration layer built directly on top of LiteRT, Google’s high-performance multi-platform runtime trusted by millions of Android and edge developers. LiteRT provides the foundational hardware acceleration via XNNPack for CPU and ML Drift for GPU. LiteRT-LM adds the specialized GenAI libraries and APIs, such as KV-cache management, prompt templating, and function calling. This integrated stack is the same technology powering the Google AI Edge Gallery showcase app.
- Qwen3-Next-80B-A3B-Instruct-MLX-5bit↗
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**: Qwen3-Next-80B-A3B-Instruct **MLX quantization**: provided by LM Studio team using mlx_lm **LM Studio Model Page**: https://lmstudio.ai/models/qwen/qwen3-next-80b 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.
- NeuralLemon-7B-GGUF↗
backyardai
*** - **Creator:** itchindigo - **Original:** NeuralLemon - **Date Created:** 2024-05-14 - **Trained Context:** 32768 tokens - **Description:** Capable 7B model which offers verbose outputs while leading the story nicely. *** GGUF is a large language model (LLM) format that can be split between CPU and GPU. GGUFs are compatible with applications based on llama.cpp, such as Backyard AI. Where other model formats require higher end GPUs with ample VRAM, GGUFs can be efficiently run on a wider variety of hardware. GGUF models are quantized to reduce resource usage, with a tradeoff of reduced coherence at lower quantizations. Quantization reduces the precision of the model weights by changing the number of bits used for each weight.
- distilbart-mnli-12-3↗
valhalla
distilbart-mnli is the distilled version of bart-large-mnli created using the **No Teacher Distillation** technique proposed for BART summarisation by Huggingface, here. If you want to train these models yourself, clone the distillbart-mnli repo and follow the steps below
- gemma-4-26B-A4B-it-litert-lm↗
litert-community
This model card provides the Gemma 4 26B (A4B) mixture-of-experts model in LiteRT-LM format that is ready for deployment on web and desktop. Please check back here regularly for updates on wider platform support and further functionality improvements. The current LiteRT-LM version supports text; audio, image, and multitoken prediction support will be available in a future update. Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. This particular Gemma 4 model is a medium size so it is ideal for desktop use cases. By running this model on device, users can have private access to Generative AI technology without even requiring an internet connection. These models are provided in the `.litertlm` format for use with the LiteRT-LM framework. LiteRT-LM is a specialized orchestration layer built directly on top of LiteRT, Google’s high-performance multi-platform runtime trusted by millions of Android and edge developers. LiteRT provides the foundational hardware acceleration via XNNPack for CPU and ML Drift for GPU. LiteRT-LM adds the specialized GenAI libraries and APIs, such as KV-cache management, prompt templating, and function calling. This integrated stack is the same technology powering the Google AI Edge Gallery showcase app.