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AI 모델 183
- HY-MT1.5-1.8B-GGUF↗
腾讯
🤗 Hugging Face | 🕹️ Demo 🤖 ModelScope | Hunyuan Translation Model Version 1.5 includes a 1.8B translation model, HY-MT1.5-1.8B, and a 7B translation model, HY-MT1.5-7B. Both models focus on supporting mutual translation across 33 languages and incorporating 5 ethnic and dialect variations. Among them, HY-MT1.5-7B is an upgraded version of our WMT25 championship model, optimized for explanatory translation and mixed-language scenarios, with newly added support for terminology intervention, contextual translation, and formatted translation. Despite having less than one-third the parameters of HY-MT1.5-7B, HY-MT1.5-1.8B delivers translation performance comparable to its larger counterpart, achieving both high speed and high quality. After quantization, the 1.8B model can be deployed on edge devices and support real-time translation scenarios, making it widely applicable. - HY-MT1.5-1.8B achieves the industry-leading performance among models of the same size, surpassing most commercial translation APIs. - HY-MT1.5-1.8B supports deployment on edge devices and real-time translation scenarios, offering broad applicability. - HY-MT1.5-7B, compared to its September open-source version, has been optimized for annotated and mixed-language scenarios. - Both models support terminology intervention, contextual translation, and formatted translation.
- Wan2.1-Fun-V1.1-14B-InP↗
alibaba-pai
V1.1: | 名称 | 存储空间 | Hugging Face | Model Scope | 描述 | |--|--|--|--|--| | Wan2.1-Fun-V1.1-1.3B-InP | 19.0 GB | 🤗Link | 😄Link | Wan2.1-Fun-V1.1-1.3B文图生视频权重，以多分辨率训练，支持首尾图预测。 | | Wan2.1-Fun-V1.1-14B-InP | 47.0 GB | 🤗Link | 😄Link | Wan2.1-Fun-V1.1-14B文图生视频权重，以多分辨率训练，支持首尾图预测。 | | Wan2.1-Fun-V1.1-1.3B-Control | 19.0 GB | 🤗Link | 😄Link| Wan2.1-Fun-V1.1-1.3B视频控制权重支持不同的控制条件，如Canny、Depth、Pose、MLSD等，支持参考图 + 控制条件进行控制，支持使用轨迹控制。支持多分辨率（512，768，1024）的视频预测，支持多分辨率（512，768，1024）的视频预测，以81帧、每秒16帧进行训练，支持多语言预测 | | Wan2.1-Fun-V1.1-14B-Control | 47.0 GB | 🤗Link | 😄Link| Wan2.1-Fun-V1.1-14B视视频控制权重支持不同的控制条件，如Canny、Depth、Pose、MLSD等，支持参考图 + 控制条件进行控制，支持使用轨迹控制。支持多分辨率（512，768，1024）的视频预测，支持多分辨率（512，768，1024）的视频预测，以81帧、每秒16帧进行训练，支持多语言预测 | | Wan2.1-Fun-V1.1-1.3B-Control-Camera | 19.0 GB | 🤗Link | 😄Link| Wan2.1-Fun-V1.1-1.3B相机镜头控制权重。支持多分辨率（512，768，1024）的视频预测，支持多分辨率（512，768，1024）的视频预测，以81帧、每秒16帧进行训练，支持多语言预测 | | Wan2.1-Fun-V1.1-14B-Control | 47.0 GB | 🤗Link | 😄Link| Wan2.1-Fun-V1.1-14B相机镜头控制权重。支持多分辨率（512，768，1024）的视频预测，支持多分辨率（512，768，1024）的视频预测，以81帧、每秒16帧进行训练，支持多语言预测 | V1.0: | 名称 | 存储空间 | Hugging Face | Model Scope | 描述 | |--|--|--|--|--| | Wan2.1-Fun-1.3B-InP | 19.0 GB | 🤗Link | 😄Link | Wan2.1-Fun-1.3B文图生视频权重，以多分辨率训练，支持首尾图预测。 | | Wan2.1-Fun-14B-InP | 47.0 GB | 🤗Link | 😄Link | Wan2.1-Fun-14B文图生视频权重，以多分辨率训练，支持首尾图预测。 | | Wan2.1-Fun-1.3B-Control | 19.0 GB | 🤗Link | 😄Link| Wan2.1-Fun-1.3B视频控制权重，支持不同的控制条件，如Canny、Depth、Pose、MLSD等，同时支持使用轨迹控制。支持多分辨率（512，768，1024）的视频预测，支持多分辨率（512，768，1024）的视频预测，以81帧、每秒16帧进行训练，支持多语言预测 | | Wan2.1-Fun-14B-Control | 47.0 GB | 🤗Link | 😄Link| Wan2.1-Fun-14B视频控制权重，支持不同的控制条件，如Canny、Depth、Pose、MLSD等，同时支持使用轨迹控制。支持多分辨率（512，768，1024）的视频预测，支持多分辨率（512，768，1024）的视频预测，以81帧、每秒16帧进行训练，支持多语言预测 | Windows 的详细信息： - 操作系统 Windows 10 - python: python3.10 & python3.11 - pytorch: torch2.2.0 - CUDA: 11.8 & 12.1 - CUDNN: 8+ - GPU： Nvidia-3060 12G & Nvidia-3090 24G
- detector-scope↗
liamdugan
- mossformer2-librimix-2spk↗
alibabasglab
source code for model inference, - github: https://github.com/alibabasglab/MossFormer2 - modelscope: https://modelscope.cn/models/iic/speech_mossformer2_separation_temporal_8k
- QwenPaw-Flash-27B-Q4_K_M↗
agentscope-ai
- speaker-diarization-coreml↗
FluidInference
Core ML conversions used by FluidAudio for on-device speaker diarization on Apple platforms. The supported Community-1 artifact set is distributed under CC-BY-4.0. See NOTICE.md for attribution and the precise license scope, and PROVENANCE.md plus provenance.json for source, conversion, environment, and file-integrity records. The snapshot includes uncompiled packages for `Segmentation`, `FBank`, `Embedding`, and `PldaRho` under `mlpackages/`; no uncompiled `PLDA` package was published. These are Core ML conversions, not fine-tuned models.
- nllb-200-distilled-600M↗
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.
- gemma-scope-2-27b-it↗
Google
- CLIP-ViT-L-scope↗
lewington
Heavily inspired by google/gemma-scope we are releasing a suite of 8 sparse autoencoders for laion/CLIP-ViT-L-14-laion2B-s32B-b82K. Training logs are available via wandb and training code is available on github. The training process is heavily reliant on AWS ECS so the weights and biases logs may contain some strange artifacts when a spot instance is killed and the training is resumed by another instance. Some of the code is ripped directly from Hugo Fry. We calculate MSE as `(batch - reconstruction).pow(2).sum(dim=-1).mean()` i.e. The MSE between the batch and the reconstruction, summed across features. We use batch norm to bring all activations into a similar range. We normalize the input before training using batch norm.
- Imgscope-OCR-2B-0527-GGUF↗
mradermacher