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AI モデル 84757
- seedream-v5.5/layer-decomposition↗
字节跳动
图生图支持最多 10 张参考图，不要求输入图带透明通道。分辨率支持 1K、1.5K、2K，也可改用宽高像素。背景可选 auto、transparent、opaque。jpeg 不能配 transparent，jpeg 配 auto 输出不透明图。水印默认关闭。提示词优化只支持 standard。不支持组图和联网搜索。
- Qwen3.8-27B-iMatrix-NVFP4-MTP-GGUF↗
cdiamond
I built this quant because the ready-made FP4 file answered the wrong question. It was fast, but on my short WikiText-2 control it scored 6.4949 PPL. Plain Q4_0 scored 6.3798. The first higher-quality hybrid went too far the other way: good perplexity, 34.19 tok/s, and no comfortable room for 256K plus vision. It is a 17.1 GB, 5.01 BPW mixed-precision GGUF of Qwen/Qwen3.8-27B. It keeps large, tolerant matrices in native NVFP4 and spends more bits on selected attention, Gated DeltaNet, and late FFN tensors. The trained MTP layer remains embedded in the same GGUF. This is not a fine-tune. I built the private calibration workload from 5,472 messages across 296 real sessions in my Hermes agent setup. The 153,600 processed tokens covered coding, infrastructure work, tool calls, and mixed Polish-English conversation. I used `llama-imatrix` to rank tensor sensitivity; the corpus and raw importance matrix are not distributed or embedded in the model.
- wav2vec2-large-xlsr-53-dutch↗
jonatasgrosman
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Dutch using the train and validation splits of Common Voice 6.1 and CSS10. When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned thanks to the GPU credits generously given by the OVHcloud :) The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
- Kronos-Tokenizer-base↗
NeoQuasar
**Kronos** is the **first open-source foundation model** for financial candlesticks (K-lines), trained on data from over **45 global exchanges**. It is designed to handle the unique, high-noise characteristics of financial data. Kronos is a family of decoder-only foundation models, pre-trained specifically for the "language" of financial markets—K-line sequences. It leverages a novel two-stage framework: 1. A specialized tokenizer first quantizes continuous, multi-dimensional K-line data (OHLCV) into **hierarchical discrete tokens**. 2. A large, autoregressive Transformer is then pre-trained on these tokens, enabling it to serve as a unified model for diverse quantitative tasks. The success of large-scale pre-training paradigm, exemplified by Large Language Models (LLMs), has inspired the development of Time Series Foundation Models (TSFMs). Kronos addresses existing limitations by introducing a specialized tokenizer that discretizes continuous market information into token sequences, preserving both price dynamics and trade activity patterns. We pre-train Kronos using an autoregressive objective on a massive, multi-market corpus of over 12 billion K-line records from 45 global exchanges, enabling it to learn nuanced temporal and cross-asset representations. Kronos excels in a zero-shot setting across a diverse set of financial tasks, including price series forecasting, volatility forecasting, and synthetic data generation.
- Unlimited-OCR↗
baidu
- [2026/07/21] 🤝 Thanks to the ms-swift community for their support, our model now supports training with ms-swift. - [2026/07/03] 🤝 Thanks to the Baidu Cloud team for their support. Our model is now available on Baidu Cloud. - [2026/06/28] 🤝 Thanks to the vLLM community and Tianyu Guo for their support, our model now supports vLLM inference. - [2026/06/24] 🤝 Thanks to AK for creating a demo for us. It is now available at Hugging Face Spaces. - [2026/06/23] 📄 Our paper is now available on arXiv. - [2026/06/23] 🤝 Thanks to the ModelScope community for their support. Our model is now available at ModelScope. - [2026/06/22] 🚀 We present Unlimited-OCR, aiming to push Deepseek-OCR one step further. Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.12.3 + CUDA12.9： **Recipe:** https://recipes.vllm.ai/baidu/Unlimited-OCR
- gemma-4-E2B-it↗
Google
Hugging Face | GitHub | Launch Blog | Documentation | Technical Report 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. Featuring both Dense and Mixture-of-Experts (MoE) architectures, Gemma 4 is well-suited for tasks like text generation, coding, and reasoning. The models are available in five distinct sizes: **E2B**, **E4B**, **12B**, **26B A4B**, and **31B**. Their diverse sizes make them deployable in environments ranging from high-end phones to laptops and servers, democratizing access to state-of-the-art AI.
- vocos-mel-24khz↗
charactr
Vocos is a fast neural vocoder designed to synthesize audio waveforms from acoustic features. Trained using a Generative Adversarial Network (GAN) objective, Vocos can generate waveforms in a single forward pass. Unlike other typical GAN-based vocoders, Vocos does not model audio samples in the time domain. Instead, it generates spectral coefficients, facilitating rapid audio reconstruction through inverse Fourier transform.
- Florence-2-base↗
Microsoft
This Hub repository contains a HuggingFace's `transformers` implementation of Florence-2 model from Microsoft. Florence-2 is an advanced vision foundation model that uses a prompt-based approach to handle a wide range of vision and vision-language tasks. Florence-2 can interpret simple text prompts to perform tasks like captioning, object detection, and segmentation. It leverages our FLD-5B dataset, containing 5.4 billion annotations across 126 million images, to master multi-task learning. The model's sequence-to-sequence architecture enables it to excel in both zero-shot and fine-tuned settings, proving to be a competitive vision foundation model. Resources and Technical Documentation: + Florence-2 technical report. + Jupyter Notebook for inference and visualization of Florence-2-large model
- Qwen3.5-2B↗
阿里巴巴 · Qwen
> [!Note] > This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc. > > In light of its parameter scale, the intended use cases are prototyping, task-specific fine-tuning, and other research or development purposes. Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency. - **Unified Vision-Language Foundation**: Early fusion training on multimodal tokens achieves cross-generational parity with Qwen3 and outperforms Qwen3-VL models across reasoning, coding, agents, and visual understanding benchmarks.
- faster-whisper-large-v3-turbo↗
dropbox-dash
This repository contains the conversion of openai/whisper-large-v3-turbo to the CTranslate2 model format. This model can be used in CTranslate2 or projects based on CTranslate2 models such as faster-whisper. It is called automatically for Mobius Labs fork of faster-whisper. Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the compute_type option in CTranslate2.