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AI 模型 183
- Krea-2-QuantFunc-4bit↗
QuantFunc
🌐 Website | 🐙 GitHub | 🤗 Hugging Face | 🤖 ModelScope | 🎨 Civitai | 🎮 Discord QuantFunc INT4 compresses Krea-2-Turbo to about a quarter of its 16-bit size. In our visual comparisons, composition, detail, color and style stay close to the 16-bit baseline, roughly on par with FP8 and INT8 ConvRot. > End-to-end includes text encoder, denoising and VAE. Text encoder and VAE are not part of the QuantFunc plugin's acceleration path today. Actual speed varies with resolution, steps, driver, software version and hardware.
- Fun-ASR-MLT-Nano-2512↗
FunAudioLLM
This model is part of the **FunASR** ecosystem — one industrial-grade open-source toolkit for **ASR · VAD · punctuation · speaker diarization · emotion / event · LLM-ASR**. A Star really helps the project (and keeps you updated): Fun-ASR-MLT-Nano-2512 is the 800M-parameter multilingual checkpoint in the Fun-ASR family. It is trained on hundreds of thousands of hours of multilingual speech and supports 31 languages, with emphasis on East and Southeast Asian languages. For the Chinese, English, and Japanese checkpoint trained on tens of millions of hours, use Fun-ASR-Nano-2512. **This repository hosts the MLT checkpoint.** Its supported-language scope differs from the standard Fun-ASR-Nano checkpoint.
- glm-5.3-exlr8-k2-k3-k4↗
kindlingai
Version 0.5 (git tag `v0.5`). See the changelog. Weight set ID: `2011c0cedde962d19fc43e74c1d409279e398d40d27a6fdb4bcb7b49ea2ea6fb` (see Identifying the weights). Also mirrored on ModelScope: kindlingai/glm-5.3-exlr8-k2-k3-k4. > **These weights cannot be used without E4M3 rounding.** A loader must round every value the trellis > codebook produces to FP8 E4M3 (round to nearest even, saturating) before applying the side vectors. The > encoder searched only over E4M3-rounded values, so the rounding is part of the format: a stock EXL3 decoder, > or any decoder that skips the rounding, reads measurably worse weights from the same bytes.
- QwenPaw-Flash-9B↗
agentscope-ai
- Jan-nano-128k-GGUF↗
unsloth
Jan-Nano-128k represents a significant advancement in compact language models for research applications. Building upon the success of Jan-Nano, this enhanced version features a **native 128k context window** that enables deeper, more comprehensive research capabilities without the performance degradation typically associated with context extension methods. **Key Improvements:** - **🔍 Research Deeper**: Extended context allows for processing entire research papers, lengthy documents, and complex multi-turn conversations - **⚡ Native 128k Window**: Built from the ground up to handle long contexts efficiently, maintaining performance across the full context range - **📈 Enhanced Performance**: Unlike traditional context extension methods, Jan-Nano-128k shows improved performance with longer contexts This model maintains full compatibility with Model Context Protocol (MCP) servers while dramatically expanding the scope of research tasks it can handle in a single session.
- Qwen-Image-FP8↗
unsloth
This is an FP8 / INT8 quantized version of Qwen-Image. - Optimized for efficient inference with reduced memory footprint. Same-seed LPIPS vs the bf16 model (lower is better): 0.057 INT8; FP8 needs activation_value_lb handling - use INT8 for stability. 💜 Qwen Chat    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Tech Report    |    📑 Blog    We are thrilled to release **Qwen-Image**, an image generation foundation model in the Qwen series that achieves significant advances in **complex text rendering** and **precise image editing**. Experiments show strong general capabilities in both image generation and editing, with exceptional performance in text rendering, especially for Chinese.
- Wan2.1-FLF2V-14B-720P↗
Wan-AI
💜 Wan    ｜    🖥️ GitHub    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Technical Report    |    📑 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. If your work has improved **Wan2.1** and you would like more people to see it, please inform us. - CFG-Zero enhances **Wan2.1** (covering both T2V and I2V models) from the perspective of CFG. - TeaCache now supports **Wan2.1** acceleration, capable of increasing speed by approximately 2x. Feel free to give it a try! - DiffSynth-Studio provides more support for **Wan2.1**, including video-to-video, FP8 quantization, VRAM optimization, LoRA training, and more. Please refer to their examples.
- Ling 3.1 Flash 1019↗
InclusionAI
🤗 Hugging Face | 🤖 ModelScope | 🐙 OpenRouter We introduce **Ling-3.1-flash-1019**, an upgraded version of Ling-3.1-flash, with **560B** total parameters, **25B** active parameters per token, and a **1M-token context window**. With further post-training optimizations, **Ling-3.1-flash-1019** demonstrates significant improvements in reasoning and real-world agentic capabilities. Its **AA-Briefcase** score rises from approximately 1400 to **1524 Elo**, while performance on **AutomationBench-AA** increases from 62% to **68%**. The model also makes gains on scientific benchmarks, including **CritPt** (18% → **26%**) and **SciCode** (54% → **57%**). Overall, it scores **44** on the **Artificial Analysis Intelligence Index v4.3.2**, up from 41 for its predecessor. These gains highlight the model's improved capabilities in complex reasoning, multi-step task execution, and agentic workflows across diverse real-world scenarios. The table and diagram below summarize the model’s specifications and architecture.
- gemma-scope-2b-pt-transcoders↗
Google
- nllb-200-distilled-1.3B↗
facebook
Here are the metrics for that particular checkpoint. - Information about training algorithms, parameters, fairness constraints or other applied approaches, and features. The exact training algorithm, data and the strategies to handle data imbalances for high and low resource languages that were used to train NLLB-200 is described in the paper. - Paper or other resource for more information NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv, 2022 - License: CC-BY-NC - Where to send questions or comments about the model: https://github.com/facebookresearch/fairseq/issues - Primary intended uses: NLLB-200 is a machine translation model primarily intended for research in machine translation, - especially for low-resource languages. It allows for single sentence translation among 200 languages. Information on how to - use the model can be found in Fairseq code repository along with the training code and references to evaluation and training data. - Primary intended users: Primary users are researchers and machine translation research community. - Out-of-scope use cases: NLLB-200 is a research model and is not released for production deployment. NLLB-200 is trained on general domain text data and is not intended to be used with domain specific texts, such as medical domain or legal domain. The model is not intended to be used for document translation. The model was trained with input lengths not exceeding 512 tokens, therefore translating longer sequences might result in quality degradation. NLLB-200 translations can not be used as certified translations.