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AI 模型 84759
- ltxv-gguf↗
calcuis
- run it with `gguf-connector`; simply execute the command below in console/terminal > >GGUF file(s) available. Select which one to use: > >1. ltxv-2b-0.9.8-distilled-iq4_nl.gguf >2. ltxv-2b-0.9.8-distilled-q6_k.gguf >4. ltxv-2b-0.9.8-distilled-q8_0.gguf > >Enter your choice (1 to 3): _ > - opt a `gguf` file in your current directory to interact with; nothing else note: for the latest update, should be able to adjust number of steps, i.e., for distilled model, use 15 instead of 30; save a lot of loading time
- bert-base-NER-GGUF↗
cstr
- orpheus-3b-base-GGUF↗
cstr
- qwen3-embed-8b-GGUF↗
cstr
- gemma-4-12B-it-AWQ-INT4↗
cyankiwi
Hugging Face | GitHub | Launch Blog | Documentation | Technical Report > [!Note] > This model card is for the Gemma 4 12B Unified model, which is part of the Gemma 4 family of open models. Built with the same multimodal functionality as Gemma 4 E2B and E4B (text, audio, image, and video inputs), it brings native audio and vision understanding directly to local environments without the need for separate encoders. This unified approach to multimodality makes the model encoder-free, offering a deployment size that is perfect for consumer devices and streamlined local execution. Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages.
- pythia-70m-seed9↗
EleutherAI
- dino-vits8↗
facebook
Vision Transformer (ViT) model trained using the DINO method. It was introduced in the paper Emerging Properties in Self-Supervised Vision Transformers by Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, Armand Joulin and first released in this repository. Disclaimer: The team releasing DINO did not write a model card for this model so this model card has been written by the Hugging Face team. The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a self-supervised fashion, namely ImageNet-1k, at a resolution of 224x224 pixels.
- EUPE-ConvNeXt-B↗
facebook
- sapiens2-pose-5b↗
facebook
308-keypoint top-down pose estimation including detailed face (274 keypoints), hand, and foot keypoints. Predictions follow the Sociopticon keypoint format. This repository contains the **5B Pose Estimation** checkpoint, finetuned from the Sapiens2-5B pretrained backbone.
- ultravox-v0_6-llama-3_1-8b↗
fixie-ai
Ultravox is a multimodal Speech LLM built around a pretrained LLM (Llama, Gemma, Qwen, etc) and a speech encoder (whisper-large-v3-turbo) backbone. Ultravox is a multimodal model that can consume both speech and text as input (e.g., a text system prompt and voice user message). The input to the model is given as a text prompt with a special ` ` pseudo-token, and the model processor will replace this magic token with embeddings derived from the input audio. Using the merged embeddings as input, the model will then generate output text as usual. In v0.6 series, ultravox models are trained on expanded Hindi speech data, resulting in significantly improved speech understanding performance on Hindi and modest degradation on other languages. Additionally, the v0.6 models are also trained on noise datasets for improved noise robustness and the ability to output a special string ``((noise))`` if the input audio is too noisy or doesn't contain clear speech.