Search FoxAI
AI Models 183
- gemma-scope-9b-pt-mlp↗
Google
- Wan2.1-T2V-14B↗
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 repository features our T2V-14B model, which establishes a new SOTA performance benchmark among both open-source and closed-source models. It demonstrates exceptional capabilities in generating high-quality visuals with significant motion dynamics. It is also the only video model capable of producing both Chinese and English text and supports video generation at both 480P and 720P resolutions.
- stormscope-goes-mrms↗
NVIDIA
- gemma-scope-2-12b-it↗
Google
- Nunchaku-Qwen-Image-EDIT-2511↗
QuantFunc
These are **quantized derivative weights** of `Qwen/Qwen-Image-Edit-2511` (**Qwen-Image-Edit-2511**). > **Disclaimer:** "Nunchaku" / "SVDQuant" name the *quantization method/format* (the open-source SVDQuant work by MIT HAN Lab, Apache-2.0). This repository is an independent re-quantization and is **not affiliated with, sponsored by, or endorsed by MIT HAN Lab or the Nunchaku project**. Official Nunchaku releases are under the `nunchaku-ai` / `nunchaku-tech` namespaces. 🤗 Hugging Face | 🤖 ModelScope | 💻 GitHub | 💬 WeChat (微信) | 🎮 Discord
- biogpt↗
Microsoft
Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.e. BERT (and its variants) and GPT (and its variants), the first one has been extensively studied in the biomedical domain, such as BioBERT and PubMedBERT. While they have achieved great success on a variety of discriminative downstream biomedical tasks, the lack of generation ability constrains their application scope. In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large-scale biomedical literature. We evaluate BioGPT on six biomedical natural language processing tasks and demonstrate that our model outperforms previous models on most tasks. Especially, we get 44.98%, 38.42% and 40.76% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks, respectively, and 78.2% accuracy on PubMedQA, creating a new record. Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fluent descriptions for biomedical terms. You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility:
- Carnice-V2-27b-GGUF↗
kai-os
GGUF exports for `kai-os/carnice-v2-27b`, a merged BF16 SFT of `Qwen/Qwen3.6-27B` for Hermes-style agent traces. For a 16GB GPU, start with `IQ2_M` if your runtime supports IQ quants and this Qwen3.5/Qwen3.6 GGUF architecture. If the runtime is older or fails to load IQ quants, use `Q2_K`. Scope note: these are source SFT checks, not separate GGUF quant benchmark scores. The full benchmark artifact bundle is in the merged model repo: `kai-os/carnice-v2-27b`.
- Qwen-Image↗
unsloth
> Mirror of Qwen/Qwen-Image, republished by Unsloth. The weights > are unmodified. See NOTICE and the licence file in this repo for the terms, which are > the upstream terms and are unchanged. 💜 Qwen Chat    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Tech Report    |    📑 Blog    🖥️ Demo    |   💬 WeChat (微信)    |   🫨 Discord   
- Qwen-Image-Edit-2509-GGUF↗
unsloth
> [!NOTE] > This is a GGUF quantized version of Qwen-Image-Edit-2509. > unsloth/Qwen-Image-Edit-2509-GGUF uses Unsloth Dynamic 2.0 methodology for SOTA performance. Important layers are upcasted to higher precision. 💜 Qwen Chat    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Tech Report    |    📑 Blog    🖥️ Demo    |   💬 WeChat (微信)    |   🫨 Discord   |    Github   
- acestep-5Hz-lm-0.6B↗
ACE-Step
Project | Hugging Face | ModelScope | Space Demo | Discord Tech Report 🚀 **ACE-Step v1.5** is a highly efficient open-source music foundation model designed to bring commercial-grade music generation to consumer hardware. * **💰 Commercial-Ready:** Unlike many models trained on ambiguous datasets, ACE-Step v1.5 is designed for creators. You can strictly use the generated music for **commercial purposes**. * **📚 Safe & Robust Training Data:** The model is trained on a massive, legally compliant dataset consisting of: * **Licensed Data:** Professionally licensed music tracks. * **Royalty-Free / No-Copyright Data:** A vast collection of public domain and royalty-free music. * **Synthetic Data:** High-quality audio generated via advanced MIDI-to-Audio conversion. * **⚡ Extreme Speed:** Generates a full song in under 2 seconds on an A100 and under 10 seconds on an RTX 3090. * **🖥️ Consumer Hardware Friendly:** Runs locally with less than 4GB of VRAM.