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AI Models 2119

  • all_datasets_v4_MiniLM-L6↗

    flax-sentence-embeddings

    The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We used the pretrained 'MiniLM-L6-H384-uncased' model and fine-tuned in on a 1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset. We developped this model during the Community week using JAX/Flax for NLP & CV, organized by Hugging Face. We developped this model as part of the project: Train the Best Sentence Embedding Model Ever with 1B Training Pairs. We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Google’s Flax, JAX, and Cloud team member about efficient deep learning frameworks. Our model is intented to be used as a sentence encoder. Given an input sentence, it ouptuts a vector which captures the sentence semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.

  • Samantha-Qwen2-7B-i1-GGUF↗

    mradermacher

  • Qwen-Image-FP8↗

    unsloth

    This is an FP8 / INT8 quantized version of Qwen-Image. - Optimized for efficient inference with reduced memory footprint. Same-seed LPIPS vs the bf16 model (lower is better): 0.057 INT8; FP8 needs activation_value_lb handling - use INT8 for stability. 💜 Qwen Chat &nbsp&nbsp | &nbsp&nbsp🤗 Hugging Face &nbsp&nbsp | &nbsp&nbsp🤖 ModelScope &nbsp&nbsp | &nbsp&nbsp 📑 Tech Report &nbsp&nbsp | &nbsp&nbsp 📑 Blog &nbsp&nbsp We are thrilled to release **Qwen-Image**, an image generation foundation model in the Qwen series that achieves significant advances in **complex text rendering** and **precise image editing**. Experiments show strong general capabilities in both image generation and editing, with exceptional performance in text rendering, especially for Chinese.

  • MERT-v1-95M↗

    m-a-p

    The development log of our Music Audio Pre-training (m-a-p) model family: - 02/06/2023: arxiv pre-print and training codes released. - 17/03/2023: we release two advanced music understanding models, MERT-v1-95M and MERT-v1-330M , trained with new paradigm and dataset. They outperform the previous models and can better generalize to more tasks. - 14/03/2023: we retrained the MERT-v0 model with open-source-only music dataset MERT-v0-public - 29/12/2022: a music understanding model MERT-v0 trained with **MLM** paradigm, which performs better at downstream tasks. - 29/10/2022: a pre-trained MIR model music2vec trained with **BYOL** paradigm. The m-a-p models share the similar model architecture and the most distinguished difference is the paradigm in used pre-training. Other than that, there are several nuance technical configuration needs to know before using: - **Model Size**: the number of parameters that would be loaded to memory. Please select the appropriate size fitting your hardware. - **Transformer Layer-Dimension**: The number of transformer layers and the corresponding feature dimensions can be outputted from our model. This is marked out because features extracted by **different layers could have various performance depending on tasks**. - **Feature Rate**: Given a 1-second audio input, the number of features output by the model. - **Sample Rate**: The frequency of audio that the model is trained with.

  • NVIDIA-Nemotron-3.5-Bolt-E4B-Pollard↗

    PollardWeights

    > ### Pollard shrank this model: **15.99 GB (f16) -> 3.10 GB** -- **81% smaller, 5.2x down**. > The smallest rung here; larger, higher-fidelity rungs are listed below. > > | format | this model's size | > |---|---:| > | f16 | 15.99 GB | > | Q8_0 | ~8.48 GB | > | Q6_K | 6.22 GB | > | Q4_K_M | ~4.64 GB | > | **PollardMix (this repo's IQ2_XXS)** | **3.10 GB** | Pollard builds of nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-BF16 made with Pollard Weights -- a ladder of **measured-allocation** quants (bits placed by per-layer sensitivity, not a uniform crush). Every rung is the **same weights**, sized to a different RAM budget by the measured allocation. Pick the largest one that fits your machine with room for context:

  • JQ2_K-JQ6_K-Test-Models↗

    eaddario

    Quantization test results for **K2-Horizon-7B** (dense) and **LFM2.5-8B-A1B** (MoE), comparing standard llama.cpp quantization against a fork that adds five new non-linear super-block types: **JQ2_K** to **JQ6_K**. On both models, four of the five new types perform materially better than their counterpart on every measured metric (mean and 99% KLD, Δp RMS, top-1 agreement). `JQ2_K` is the exception. The size of the gain depends on the model: on the dense `K2-Horizon-7B`, `JQ3_K` and `JQ6_K` cut the mean KLD by more than half, while on the MoE `LFM2.5-8B-A1B` the improvements range from 13.8% to 23.2%. The five new types are non-linear (NL) super-block (K) types. Each new type spends exactly the same index payload as its K-quant counterpart:

  • SambaLingo-Serbian-Base-i1-GGUF↗

    mradermacher

  • Qwen-image_SamsungCam_UltraReal↗

    Danrisi

  • resnet18.a3_in1k↗

    timm

    This model features: * ReLU activations * single layer 7x7 convolution with pooling * 1x1 convolution shortcut downsample Recipe details: * ResNet Strikes Back `A3` recipe * LAMB optimizer with BCE loss * Cosine LR schedule with warmup - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 11.7 - GMACs: 0.9 - Activations (M): 1.3 - Image size: train = 160 x 160, test = 224 x 224 - **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 - **Original:** https://github.com/huggingface/pytorch-image-models

  • Qwen3.8-27B-MTP-GGUF↗

    Jackrong

    This release bundles the MTP head directly into the model, enabling speculative decoding without loading a separate draft model. > 📌 Results are from an internal benchmark using identical runtime, GPU, > Flash Attention, and sampling settings. Actual performance may vary > depending on prompt length, reasoning depth, output length, and hardware. > [!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, TokenSpeed, etc.