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  • distilhubert↗

    ntu-spml

    The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for more in-detail explanation of how to fine-tune the model. Paper: DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT

  • Seedream 5.0 Lite↗

    字节跳动

    Seedream 5.0 Lite API（APIMart）：生成 4K AI 图像，文字渲染先进，单批最多 15 张，支持 10 张参考图。开通会员，API 调用立省20%起。

  • gemma-3-12b-it-qat-q4_0-unquantized↗

    Lightricks

    > [!Note] > This repository corresponds to the 12B **instruction-tuned** version of the Gemma 3 model using Quantization Aware Training (QAT). > > **The checkpoint in this repository is unquantized, please make sure to quantize with Q4_0 with your favorite tool** > > Thanks to QAT, the model is able to preserve similar quality as `bfloat16` while significantly reducing the memory requirements > to load the model. * [Gemma 3 Technical Report][g3-tech-report] * [Responsible Generative AI Toolkit][rai-toolkit] * [Gemma on Kaggle][kaggle-gemma] * [Gemma on Vertex Model Garden][vertex-mg-gemma3] Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone.

  • lwdetr_small_60e_coco↗

    AnnaZhang

    LW-DETR, a Light-Weight DEtection TRansformer model, is designed to be a real-time object detection alternative that outperforms conventional convolutional (YOLO-style) and earlier transformer-based (DETR) methods in terms of speed and accuracy trade-off. It was introduced in the paper LW-DETR: A Transformer Replacement to YOLO for Real-Time Detection by Chen et al. and first released in this repository. Disclaimer: This model was originally contributed by stevenbucaille in 🤗 transformers. LW-DETR is an end-to-end object detection model that uses a Vision Transformer (ViT) backbone as its encoder, a simple convolutional projector, and a shallow DETR decoder. The core philosophy is to leverage the power of transformers while implementing several efficiency-focused techniques to achieve real-time performance. Key Architectural Details: - ViT Encoder: Uses a plain ViT architecture. To reduce the quadratic complexity of global self-attention, it adopts interleaved window and global attentions. - Window-Major Organization: It employs a highly efficient window-major feature map organization scheme for attention computation, which drastically reduces the costly memory permutation operations required when transitioning between global and window attention modes, leading to lower inference latency. - Feature Aggregation: It aggregates features from multiple levels (intermediate and final layers) of the ViT encoder to create richer input for the decoder. - Projector: A C2f block (from YOLOv8) connects the encoder and decoder. For larger versions (large/xlarge), it outputs two-scale features ($1/8$ and $1/32$) to the decoder. - Shallow DETR Decoder: It uses a computationally efficient 3-layer transformer decoder (instead of the standard 6 layers), incorporating deformable cross-attention for faster convergence and lower latency. - Object Queries: It uses a mixed-query selection scheme to form the object queries from both learnable content queries and generated spatial queries (based on top-K features from the Projector).

  • Qwen2.5-7B-Instruct-abliterated-v2↗

    huihui-ai

    This is an uncensored version of Qwen/Qwen2.5-7B-Instruct created with abliteration (see this article to know more about it). Special thanks to @FailSpy for the original code and technique. Please follow him if you're interested in abliterated models. **Important Note** This version is an improvement over the previous one Qwen2.5-7B-Instruct-abliterated.

  • VieNeu-TTS-v3-Turbo-GGUF↗

    phuocnguyen90

    This repository contains the official GGUF quantized model package for **VieNeu-TTS v3 Turbo (48 kHz)**, fully integrated and supported natively in **audio.cpp** (merged in PR #80). > **Model Credits & Acknowledgments**: This is a GGUF quantization of the original VieNeu-TTS-v3-Turbo model created by **pnnbao-ump**. All credit for the underlying architecture and model training goes to the original authors. It packs both the compressed Qwen3 talker backbone and the speech tokenizer/decoder weights into a single optimized **163 MB** file, enabling instant load times and ultra-fast, torch-free C++ generation on CPU.

  • Qwen3.8-Flash-Next-Orca-Abliterated-GSQ-RCO-Coder-GGUF↗

    Abiray

    A standalone, **weight-baked uncensored & abliterated GGUF** of **`ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF`.** This build is created by precisely transplanting 144 orthogonalized residual-writing tensors from **`orcarouter/Qwen3.8-Flash-Next-Uncensored`** (via `SC117/Qwen3.8-Flash-Next-GSQ-RCO-abliterated-GGUF`) directly into the optimized 256-expert pruned Coder architecture. `ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF` compresses `Qwen3.8-Flash-Next` from 354 GB (BF16, 512 experts per layer) down to **58.4 GB** by combining: 1. **Structured Expert Pruning ($512 \rightarrow 256$ experts per layer):** Selects the 256 experts specialized for software engineering, coding, agentic tool calling, and vision. 2. **GSQ (Gumbel-Softmax Quantization) + RCO (Riemannian Co-design Search):** Assigns optimal mixed-precision GGUF quantization types across individual tensors while storing the retained weights at `~3.5 bits` (`IQ3_S`-equivalent fidelity, labeled `IQ1_M` for its `1.89` effective bits per parameter relative to the unpruned 512-expert model). --- Because this is an ultra-low-bit MoE model (`IQ1_M` effective / `IQ3_S` retained weights), **avoid pure greedy decoding (`--temp 0.0` with no repetition penalty)** on repetitive C/C++ struct definitions. Use standard Qwen sampling parameters with a light repetition/presence penalty:

  • Qwen3-Next-80B-A3B-Instruct-MLX-4bit↗

    lmstudio-community

    _👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord_. **Model creator**: Qwen **Original model**: Qwen3-Next-80B-A3B-Instruct **MLX quantization**: provided by LM Studio team using mlx_lm **LM Studio Model Page**: https://lmstudio.ai/models/qwen/qwen3-next-80b LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.

  • GRM-2.6-Plus-GGUF↗

    morikomorizz

    GRM-2.6-Plus is a highly capable **27B-parameter reasoning model** built on the Qwen3.6 architecture. It is specifically engineered for **general-purpose AI** and optimized for **difficult, high-complexity tasks**. These GGUF files are fully compatible with llama.cpp and popular graphical interfaces like **LM Studio**, **Ollama**. ```bash ./llama-cli -m GRM-2.6-Plus-Q8_0.gguf \ -p "System: You are a helpful assistant.\nUser: Create a calculator in a single HTML file backwards.\nAssistant:" \ -n 2048 -c 8192

  • finegrain-box-segmenter↗

    finegrain

    If you want to give the Finegrain Box Segmenter a try, the best way to is take a look at the Finegrain Object Cutter Space we shipped on Hugging Face: it's a fun "prompt to cut out" experience that will enable you to create pixel quality and high resolution cutouts for any object in a photo, by just naming the object. While building Finegrain, we needed a way to create pixel perfect and high resolution cutouts for objects in images. We looked at off-the-shelf solutions, but they simply didn't work for us: The Finegrain Box Segmenter avoids these pitfalls by training MVANet to be a box-promptable High Definition (1024x1024) object-cutout model, making no assumption on what is background and what is foreground: users are fully in control.