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AI 模型 183
- Jan-code-4b↗
janhq
**Jan-Code-4B** is a **code-tuned** model built on top of Jan-v3-4B-base-instruct. It’s designed to be a practical coding model you can run locally and iterate on quickly—useful for everyday code tasks and as a lightweight “worker” model in agentic workflows. Compared to larger coding models, Jan-Code focuses on handling **well-scoped subtasks** reliably while keeping latency and compute requirements small. Jan-code is optimized for direct integration with Jan Desktop, select the model in the app to start using it.
- Wan2.2-S2V-14B↗
Wan-AI
This repository features the **Wan2.2-S2V-14B** model, designed for audio-driven cinematic video generation. It was introduced in the paper: **Wan-S2V: Audio-Driven Cinematic Video Generation** 💜 Wan Homepage    ｜    🖥️ GitHub    |   🤗 Hugging Face Organization    |   🤖 ModelScope Organization    |    📑 Wan-S2V Paper    |    📑 Wan2.2 Base Paper    | 🌐 Project Page    |    📑 Blog    |    💬 Discord    📕 使用指南(中文)    |    📘 User Guide(English)    |   💬 WeChat(微信)   
- Wan2.2-TI2V-5B↗
Wan-AI
💜 Wan    ｜    🖥️ GitHub    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Technical Report    |    📑 Blog    |   💬 WeChat Group    |    📖 Discord    We are excited to introduce **Wan2.2**, a major upgrade to our foundational video models. With **Wan2.2**, we have focused on incorporating the following innovations: - 👍 **Effective MoE Architecture**: Wan2.2 introduces a Mixture-of-Experts (MoE) architecture into video diffusion models. By separating the denoising process cross timesteps with specialized powerful expert models, this enlarges the overall model capacity while maintaining the same computational cost.
- Inkscope-Captions-2B-0526-i1-GGUF↗
mradermacher
- gemma-scope-9b-pt-att↗
Google
- gemma-scope-transcoders↗
mwhanna
- Wan2.1-I2V-14B-480P↗
Wan-AI
💜 Wan    ｜    🖥️ GitHub    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Paper (Coming soon)    |    📑 Blog    |   💬 WeChat Group    |    📖 Discord    In this repository, we present **Wan2.1**, a comprehensive and open suite of video foundation models that pushes the boundaries of video generation. **Wan2.1** offers these key features: - 👍 **SOTA Performance**: **Wan2.1** consistently outperforms existing open-source models and state-of-the-art commercial solutions across multiple benchmarks. - 👍 **Supports Consumer-grade GPUs**: The T2V-1.3B model requires only 8.19 GB VRAM, making it compatible with almost all consumer-grade GPUs. It can generate a 5-second 480P video on an RTX 4090 in about 4 minutes (without optimization techniques like quantization). Its performance is even comparable to some closed-source models. - 👍 **Multiple Tasks**: **Wan2.1** excels in Text-to-Video, Image-to-Video, Video Editing, Text-to-Image, and Video-to-Audio, advancing the field of video generation. - 👍 **Visual Text Generation**: **Wan2.1** is the first video model capable of generating both Chinese and English text, featuring robust text generation that enhances its practical applications. - 👍 **Powerful Video VAE**: **Wan-VAE** delivers exceptional efficiency and performance, encoding and decoding 1080P videos of any length while preserving temporal information, making it an ideal foundation for video and image generation. This repo contains our I2V-14B model, which is capable of generating 480P videos, offering advantages in terms of fast generation and excellent quality.
- 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.
- Wan2.2-I2V-A14B-GGUF↗
bullerwins
You need to download both a high-noise model and a low-noise model. High noise is used for the first steps and the low-noise for the details. Place them on ComfyUI/models/unet You will need ComfyUI-GGUF from city96 💜 Wan    ｜    🖥️ GitHub    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Technical Report    |    📑 Blog    |   💬 WeChat Group    |    📖 Discord   
- emotion2vec_plus_large↗
emotion2vec
emotion2vec+ is a series of foundational models for speech emotion recognition (SER). We aim to train a "whisper" in the field of speech emotion recognition, overcoming the effects of language and recording environments through data-driven methods to achieve universal, robust emotion recognition capabilities. The performance of emotion2vec+ significantly exceeds other highly downloaded open-source models on Hugging Face. This version (emotion2vec_plus_large) uses a large-scale pseudo-labeled data for finetuning to obtain a large size model (~300M), and currently supports the following categories: 0: angry 1: disgusted 2: fearful 3: happy 4: neutral 5: other 6: sad 7: surprised 8: unknown GitHub Repo: emotion2vec |Model|⭐Model Scope|🤗Hugging Face|Fine-tuning Data (Hours)| |:---:|:-------------:|:-----------:|:-------------:| |emotion2vec|Link|Link|/| emotion2vec+ seed|Link|Link|201| emotion2vec+ base|Link|Link|4788| emotion2vec+ large|Link|Link|42526|