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AI Models 2119

  • SambaLingo-Japanese-Base-GGUF↗

    mradermacher

  • resnet50.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): 25.6 - GMACs: 4.1 - Activations (M): 11.1 - Image size: 224 x 224 - **Papers:** - 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.

  • Qwen3.8-27B-GGUF↗

    Arki05

    10 GGUF files of Qwen/Qwen3.8-27B at the sizes of Unsloth's dynamic quants and of ISTA-DASLab's GSQ-RCO quants, with 16–38 % less KL divergence than the Unsloth file of the same size and 11–26 % less than ISTA-DASLab's. Two things make the difference: **Build your own size:** the qalloc app solves the mix for any size you pick and assembles the GGUF in your browser, tensor by tensor from the store. Per token, against the BF16 model: the mean KL divergence and its 99th percentile (lower is better), and how often the most likely next token is the BF16 model's (higher is better), for the published file of the same size → this file.

  • FLUX.2-klein-9b-kv-fp8↗

    Black Forest Labs

    `FLUX.2 [klein] 9B-KV` is an optimized variant of FLUX.2 [klein] 9B with **KV-cache support for accelerated multi-reference editing**. This variant caches key-value pairs from reference images during the first denoising step, eliminating redundant computation in subsequent steps for significantly faster multi-image editing workflows. For more information about FLUX.2 [klein], please read our blog post. 1. **KV-Cache Optimization**: Reference image KV pairs are computed once and cached, reducing computation and speeding up inference by up to 2.5 times for multi-reference editing tasks. 2. All capabilities of FLUX.2 [klein] 9B: sub-second generation, text-to-image, and multi-reference editing in a single unified model. 3. Ideal for interactive applications and real-time editing pipelines where the same reference images are used across multiple generations. 4. 9B flow model with 8B Qwen3 text embedder, step-distilled to 4 inference steps. 5. Available for non-commercial use.

  • CausalLM-7B-GGUF↗

    TheBloke

    **NOTE**: The GGUFs originally uploaded here did not work due to a vocab issue. This was fixed on 23rd October, 15:00 UTC. The files uploaded now are confirmed to work. 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. The creator of the source model has listed its license as `wtfpl`, and this quantization has therefore used that same license.

  • Sakura-FLUX.3-Image-Klein-PromptUpsampler-Uncensored-GGUF↗

    webmp3

    **Only the prompt upsampler is edited.** black-forest-labs/flux-3-image-klein-pup is a 9B vision-language model (fine-tuned from Qwen3.5-9B by Black Forest Labs) that expands a short image request into the detailed caption the FLUX.3 image model works best with. This repository has it in a version whose **language weights we edited with an abliteration run** so that it refuses less, as GGUF with importance-matrix quantizations. The image model and its text encoder are **not** part of this repository: see Sakura-FLUX.3-Image-Klein-GGUF (image model, official text encoder) and Sakura-FLUX.3-Image-Klein-TextEncoder-Uncensored-GGUF (uncensored text encoder). For adults only; you are responsible for what you generate and for the laws that apply to you. Independent work, not made or endorsed by Black Forest Labs. Apache-2.0 (copy in `LICENSE`). **The upsampler is optional.** FLUX.3 Klein takes any prompt directly; all example images in the FLUX repositories were made without an upsampler. We did not measure whether the upsampler improves images. Memory: with Q8_0 about 8.9 GiB of weights (plus 0.9 GiB for the projector and the context); we did not measure peak memory.

  • Qwen3.8-27B-GGUF↗

    AtomicChat

    Built from Qwen's original weights with our own importance matrix. The calibration corpora behind our builds are public. Learn how to run Qwen3.8 27B locally - read our guide . You can now run Qwen3.8 in Atomic Chat with toggles for thinking. See our quantization analysis below for measurements and instructions. Every quantization of this model we could find, ours and everyone else's, measured against the original weights on the same held-out text. The dashed line is the best file available at each size from anyone but us.

  • BioLinkBERT-base↗

    michiyasunaga

    BioLinkBERT-base model pretrained on PubMed abstracts along with citation link information. It is introduced in the paper LinkBERT: Pretraining Language Models with Document Links (ACL 2022). The code and data are available in this repository. This model achieves state-of-the-art performance on several biomedical NLP benchmarks such as BLURB and MedQA-USMLE. LinkBERT is a transformer encoder (BERT-like) model pretrained on a large corpus of documents. It is an improvement of BERT that newly captures **document links** such as hyperlinks and citation links to include knowledge that spans across multiple documents. Specifically, it was pretrained by feeding linked documents into the same language model context, besides a single document.

  • wav2vec2-large-960h↗

    facebook

    The large model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. 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.

  • TinyLlama-1.1B-step-50K-105b↗

    TinyLlama

    The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion tokens**. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01. We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint. We will be rolling out intermediate checkpoints following the below schedule. We also include some baseline models for comparison.