Saved toolsSubmit a tool
FOXAI SEARCH

Search FoxAI

AI Models 450

  • 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.

  • M2V_base_output↗

    minishlab

    This Model2Vec model is a distilled version of the baai/bge-base-en-v1.5 Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. Model2vec creates a small, fast, and powerful model that outperforms other static embedding models by a large margin on all tasks we could find, while being much faster to create than traditional static embedding models such as GloVe. Best of all, you don't need any data to distill a model using Model2Vec. It works by passing a vocabulary through a sentence transformer model, then reducing the dimensionality of the resulting embeddings using PCA, and finally weighting the embeddings using zipf weighting. During inference, we simply take the mean of all token embeddings occurring in a sentence.

  • wav2vec2-large-robust↗

    facebook

    The large model pretrained on 16kHz sampled speech audio. Speech datasets from multiple domains were used to pretrain the model: - Libri-Light: open-source audio books from the LibriVox project; clean, read-out audio data - CommonVoice: crowd-source collected audio data; read-out text snippets - Switchboard: telephone speech corpus; noisy telephone data - Fisher: conversational telephone speech; noisy telephone data **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. Authors: Wei-Ning Hsu, Anuroop Sriram, Alexei Baevski, Tatiana Likhomanenko, Qiantong Xu, Vineel Pratap, Jacob Kahn, Ann Lee, Ronan Collobert, Gabriel Synnaeve, Michael Auli

  • Mistral-Nemo-Instruct-2407-abliterated-GGUF↗

    QuantFactory

    This is quantized version of natong19/Mistral-Nemo-Instruct-2407-abliterated created using llama.cpp Abliterated version of Mistral-Nemo-Instruct-2407, a Large Language Model (LLM) trained jointly by Mistral AI and NVIDIA that significantly outperforms existing models smaller or similar in size. The model's strongest refusal directions have been ablated via weight orthogonalization, but the model may still refuse your request, misunderstand your intent, or provide unsolicited advice regarding ethics or safety.

  • gpt2-xl↗

    openai-community

    - Model Details - How To Get Started With the Model - Uses - Risks, Limitations and Biases - Training - Evaluation - Environmental Impact - Technical Specifications - Citation Information - Model Card Authors **Model Description:** GPT-2 XL is the **1.5B parameter** version of GPT-2, a transformer-based language model created and released by OpenAI. The model is a pretrained model on English language using a causal language modeling (CLM) objective. - **Developed by:** OpenAI, see associated research paper and GitHub repo for model developers. - **Model Type:** Transformer-based language model - **Language(s):** English - **License:** Modified MIT License - **Related Models:** GPT-2, GPT-Medium and GPT-Large - **Resources for more information:** - Research Paper - OpenAI Blog Post - GitHub Repo - OpenAI Model Card for GPT-2 - OpenAI GPT-2 1.5B Release Blog Post - Test the full generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large

  • bark-small↗

    suno

    Bark is a transformer-based text-to-audio model created by Suno. Bark can generate highly realistic, multilingual speech as well as other audio - including music, background noise and simple sound effects. The model can also produce nonverbal communications like laughing, sighing and crying. To support the research community, we are providing access to pretrained model checkpoints ready for inference. This model is meant for research purposes only. The model output is not censored and the authors do not endorse the opinions in the generated content. Use at your own risk. 2. Run inference via the `Text-to-Speech` (TTS) pipeline. You can infer the bark model via the TTS pipeline in just a few lines of code!