Saved toolsSubmit a tool
FOXAI SEARCH

Search FoxAI

AI Models 2119

  • wav2vec2-large↗

    facebook

    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 that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition. Check out this blog for more information. **Abstract** We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler. wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned. Experiments using all labeled data of Librispeech achieve 1.8/3.3 WER on the clean/other test sets. When lowering the amount of labeled data to one hour, wav2vec 2.0 outperforms the previous state of the art on the 100 hour subset while using 100 times less labeled data. Using just ten minutes of labeled data and pre-training on 53k hours of unlabeled data still achieves 4.8/8.2 WER. This demonstrates the feasibility of speech recognition with limited amounts of labeled data. The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20.

  • bert-base-german-dbmdz-uncased↗

    google-bert

    This model is the same as dbmdz/bert-base-german-uncased. See the dbmdz/bert-base-german-cased model card for details on the model.

  • resnet34d.ra2_in1k↗

    timm

    This model features: * ReLU activations * 3-layer stem of 3x3 convolutions with pooling * 2x2 average pool + 1x1 convolution shortcut downsample Recipe details: * RandAugment `RA2` recipe. Inspired by and evolved from EfficientNet RandAugment recipes. Published as `B` recipe in ResNet Strikes Back. * RMSProp (TF 1.0 behaviour) optimizer, EMA weight averaging * Step (exponential decay w/ staircase) LR schedule with warmup - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 21.8 - GMACs: 3.9 - Activations (M): 4.5 - Image size: train = 224 x 224, test = 288 x 288 - **Papers:** - ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476 - Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385 - Bag of Tricks for Image Classification with Convolutional Neural Networks: https://arxiv.org/abs/1812.01187 - **Original:** https://github.com/huggingface/pytorch-image-models

  • Wan-Move-14B-480P-GGUF↗

    vantagewithai

    **Wan-Move: Motion-controllable Video Generation via Latent Trajectory Guidance [Paper]** Ruihang Chu, Yefei He, Zhekai Chen, Shiwei Zhang, Xiaogang Xu, Bin Xia, Dingdong Wang, Hongwei Yi, Xihui Liu, Hengshuang Zhao, Yu Liu, Yingya Zhang, Yujiu Yang Wan-Move spports diverse motion control applications in image-to-video generation. The generated samples (832×480p, 5s) exhibits high visual fidelity and accurate motion. The models in this repository are licensed under the Apache 2.0 License. We claim no rights over the your generated contents, granting you the freedom to use them while ensuring that your usage complies with the provisions of this license. You are fully accountable for your use of the models, which must not involve sharing any content that violates applicable laws, causes harm to individuals or groups, disseminates personal information intended for harm, spreads misinformation, or targets vulnerable populations. For a complete list of restrictions and details regarding your rights, please refer to the full text of the license.

  • Qwen3.8-27B-Quark-AWQ-MXFP4↗

    amd

    - **Weights:** MXFP4 (OCP microscaling FP4, group size 32, E8M0 shared scale) - **Activations:** MXFP4, dynamic (per-block, computed at runtime) - **Algorithm:** AWQ - **Calibration:** 128 samples, seq_len 512, `pileval` dataset (Quark default) - **Base model:** Qwen/Qwen3.8-27B (bf16) - **Vision tower:** left unquantized (bf16) — only the language-model decoder (`model.language_model.layers`) is quantized; `model.visual.*` is excluded Recovery % is computed on the flexible-extract metric (`this model / bf16 base`). Non-thinking mode: the raw-completions endpoint used above doesn't support passing `enable_thinking=False` through a chat template, so non-thinking mode is approximated with a custom task that pre-closes an empty ` ` block in the prompt:

  • Nous-Hermes-Llama2-GGUF↗

    TheBloke

    GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible. The creator of the source model has listed its license as `['mit']`, and this quantization has therefore used that same license. As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.

  • Llama-3.1-Swallow-8B-Instruct-v0.3↗

    tokyotech-llm

    Llama 3.1 Swallow is a series of large language models (8B, 70B) that were built by continual pre-training on the Meta Llama 3.1 models. Llama 3.1 Swallow enhanced the Japanese language capabilities of the original Llama 3.1 while retaining the English language capabilities. We use approximately 200 billion tokens that were sampled from a large Japanese web corpus (Swallow Corpus Version 2), Japanese and English Wikipedia articles, and mathematical and coding contents, etc (see the Training Datasets section of the base model) for continual pre-training. The instruction-tuned models (Instruct) were built by supervised fine-tuning (SFT) on the synthetic data specially built for Japanese. See the Swallow Model Index section to find other model variants. **Note**: Llama-3.1-Swallow-8B-Instruct-v0.3 model was continually pre-trained from the meta-llama/Llama-3.1-8B-Instruct and then instruction-tuned with our instruction datasets. This release enhances the conversation capability of Llama 3.1 Swallow. The updated model, Llama-3.1-Swallow-8B-Instruct-v0.3 generates helpful and detailed responses based on user instructions and conversation history. Among all open-source LLMs with <= 8 billion parameters, Llama-3.1-Swallow-8B-Instruct-v0.3 exhibits **state-of-the-art performance on Japanese MT-Bench**, outperforming its predecessor, Llama-3.1-Swallow-8B-Instruct-v0.2, by 8.4 points.

  • Qwen3.5-35B-A3B-APEX-GGUF↗

    mudler

    ⚡ Each donation = another big MoE quantized I host 25+ free APEX MoE quantizations as independent research. My only local hardware is an NVIDIA DGX Spark (122 GB unified memory), enough for ~30-50B-class MoEs, but bigger ones (200B+) require rented compute on H100/H200/Blackwell, typically $20-100 per quant. If APEX quants are useful to you, your support directly funds those bigger runs. **Brought to you by the LocalAI team** -- the creators of LocalAI the open-source AI engine that runs any model - LLMs, vision, voice, image, video - on any hardware. No GPU required. APEX (Adaptive Precision for EXpert Models) is a **novel quantization technique** for Mixture-of-Experts language models. Unlike uniform quantization methods that apply the same precision to every tensor, APEX introduces a **layer-wise precision gradient** combined with **MoE-aware tensor classification** and **diverse imatrix calibration** to achieve Q8_0-level quality at a fraction of the size. The method was discovered through systematic human-driven, AI-assisted research across 25+ quantization strategies. APEX outperforms Unsloth Dynamic 2.0 (UD) quantizations on accuracy benchmarks while being 2x smaller.

  • sam3-tracker-ONNX↗

    onnx-community

  • VoxCPM2↗

    openbmb

    **VoxCPM2** is a tokenizer-free, diffusion autoregressive Text-to-Speech model — **2B parameters**, **30 languages**, **48kHz** audio output, trained on over **2 million hours** of multilingual speech data. - 🌍 **30-Language Multilingual** — No language tag needed; input text in any supported language directly - 🎨 **Voice Design** — Generate a novel voice from a natural-language description alone (gender, age, tone, emotion, pace…); no reference audio required - 🎛️ **Controllable Cloning** — Clone any voice from a short clip, with optional style guidance to steer emotion, pace, and expression while preserving timbre - 🎙️ **Ultimate Cloning** — Provide reference audio + its transcript for audio-continuation cloning; every vocal nuance faithfully reproduced - 🔊 **48kHz Studio-Quality Output** — Accepts 16kHz reference; outputs 48kHz via AudioVAE V2's built-in super-resolution, no external upsampler needed - 🧠 **Context-Aware Synthesis** — Automatically infers appropriate prosody and expressiveness from text content - ⚡ **Real-Time Streaming** — RTF as low as ~0.3 on NVIDIA RTX 4090, and ~0.13 accelerated by Nano-VLLM - 📜 **Fully Open-Source & Commercial-Ready** — Apache-2.0 license, free for commercial use Arabic, Burmese, Chinese, Danish, Dutch, English, Finnish, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Khmer, Korean, Lao, Malay, Norwegian, Polish, Portuguese, Russian, Spanish, Swahili, Swedish, Tagalog, Thai, Turkish, Vietnamese