我的收藏提交工具
FOXAI SEARCH

全站搜索

AI 模型 2119

  • DeepSeek-V4-Pro-Qwen3.5-9B-MTP-GGUF↗

    Jackrong

    🔬 DeepSeek-V4-Pro Distillation 🧠 9B Parameters 📐 ~250K Math & STEM Samples ⚡ MTP & NVFP4 Evaluated 💡 What is DeepSeek-V4-Pro-Qwen3.5-9B? DeepSeek-V4-Pro-Qwen3.5-9B is a reasoning-focused fine-tune of Qwen3.5-9B , distilled from responses generated by DeepSeek-V4-Pro in Max Effect mode . The release concentrates its supervised signal on mathematics and STEM problem solving while preserving the efficiency and practical deployability of the 9B parameter class. 🧩 Structured Reasoning Learns rigorous problem decomposition and multi-step solution patterns from a strong teacher.

  • sepformer-whamr16k↗

    speechbrain

    This repository provides all the necessary tools to perform audio source separation with a SepFormer model, implemented with SpeechBrain, and pretrained on WHAMR! dataset with 16k sampling frequency, which is basically a version of WSJ0-Mix dataset with environmental noise and reverberation in 16k. For a better experience we encourage you to learn more about SpeechBrain. The given model performance is 13.5 dB SI-SNRi on the test set of WHAMR! dataset. The system expects input recordings sampled at 16kHz (single channel). If your signal has a different sample rate, resample it (e.g, using torchaudio or sox) before using the interface. To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method.

  • speechless-code-mistral-7b-v1.0↗

    uukuguy

    Use the following dataset to fine-tune mistralai/Mistral-7B-v0.1 in order to improve the model's reasoning and planning abilities. Total 201,981 samples. - jondurbin/airoboros-2.2: Filter categories related to coding, reasoning and planning. 23,462 samples. - Open-Orca/OpenOrca: Filter the 'cot' category in 1M GPT4 dataset. 74,440 samples. - garage-bAInd/Open-Platypus: 100%, 24,926 samples. - WizardLM/WizardLM_evol_instruct_V2_196k: Coding coversation part. 30,185 samples - TokenBender/python_eval_instruct_51k: “python” in output .40,309 samples - Spider: 8,659 samples

  • privacy-filter-nemotron-v2↗

    OpenMed

    `OpenMed/privacy-filter-nemotron-v2` is the second-generation Nemotron-schema checkpoint in the OpenMed privacy-filter family. It keeps the same fine-grained 55-category PII vocabulary as `OpenMed/privacy-filter-nemotron`, while using a broader training mix and a more recall-oriented adaptation recipe. In practice, this v2 checkpoint should perform better as a general PII masking and redaction model while preserving the useful typed labels from the original Nemotron model. The model is based on `openai/privacy-filter`, a 1.4B-parameter MoE token classifier with roughly 50M active parameters per token. It predicts 221 BIOES token classes: Use this checkpoint when you want the Nemotron fine-grained label schema, but prefer the improved v2 masking behavior.

  • Qwen-Image-2.1-Turbo-mflux-6bit↗

    OsaurusAI

    An Osaurus 6-bit mflux quantization of Qwen/Qwen-Image-2.1-Turbo for Apple Silicon (14 GB, balanced size and quality). This is not an official Qwen release and is not endorsed by the Qwen team. Turbo is the 8-step distilled checkpoint of Qwen-Image-2.1. One bundle does both **text-to-image** and **reference editing** (up to 10 ordered reference images); the vision encoder is included. Requires **Osaurus 0.25.21 or newer**. Older versions treat this bundle as Qwen-Image-2.1 and sample it on that model's 40-step schedule, which gives degraded images.

  • vad-crdnn-libriparty↗

    speechbrain

    This repository provides all the necessary tools to perform voice activity detection with SpeechBrain using a model pretrained on Libriparty. The pre-trained system can process short and long speech recordings and outputs the segments where speech activity is detected. The output of the system looks like this: The system expects input recordings sampled at 16kHz (single channel). If your signal has a different sample rate, resample it (e.g., using torchaudio or sox) before using the interface.

  • Falcon3-1B-Instruct↗

    tiiuae

    **Falcon3** family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B parameters. This repository contains the **Falcon3-1B-Instruct**. It achieves strong results on reasoning, language understanding, instruction following, code and mathematics tasks. Falcon3-1B-Instruct supports 4 languages (English, French, Spanish, Portuguese) and a context length of up to 8K. We report in the following table our internal pipeline benchmarks. - We use lm-evaluation harness. - We report **raw scores** obtained by applying chat template and fewshot_as_multiturn. - We use same batch-size across all models.

  • test-gguf-moe-sample↗

    SzymonOzog

  • wide_resnet101_2.tv_in1k↗

    timm

    This model features: * ReLU activations * single layer 7x7 convolution with pooling * 1x1 convolution shortcut downsample - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 126.9 - GMACs: 22.8 - Activations (M): 21.2 - Image size: 224 x 224 - **Papers:** - Wide Residual Networks: https://arxiv.org/abs/1605.07146 - Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385 - **Original:** https://github.com/pytorch/vision Explore the dataset and runtime metrics of this model in timm model results.

  • Yi-1.5-34B↗

    01-ai

    Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples. Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension. Model | Context Length | Pre-trained Tokens | :------------: | :------------: | :------------: | | Yi-1.5 | 4K, 16K, 32K | 3.6T