Open-Weight Models

Model · Audio classification

discogs-maest-10s-pw-129e

Music Technology Group (Universitat Pompeu Fabra)

MAEST is a family of Transformer models based on PASST and focused on music analysis applications. The MAEST models are also available for inference in the Essentia library and for inference and training in the official repository. You can try the MAEST interactive demo on replicate. MAEST is a music audio representation model pre-trained on the task of music style classification. According to the evaluation reported in the original paper, it reports good performance in several downstream music analysis tasks. The MAEST models can make predictions for a taxonomy of 400 music styles derived from the public metadata of Discogs. The MAEST models have reported good performance in downstream…

Model · Summarization

pegasus-cnn_dailymail

Google

Original TF 1 code here Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019 The following is copied from the authors' README. We train a pegasus model with sampled gap sentence ratios on both C4 and HugeNews, and stochastically sample important sentences. The updated the results are reported in this table. The "Mixed & Stochastic" model has the following changes: - trained on both C4 and HugeNews (dataset mixture is weighted by their number of examples). - trained for 1.5M instead of 500k (we observe slower convergence on pretraining perplexity). - the model uniformly sample a gap sentence ratio between 15% and 45%. - importance sentences are sampled using a…

Model · Audio classification

Common-Voice-Gender-Detection-ONNX

Prithiv Sakthi

This is an ONNX version of prithivMLmods/Common-Voice-Gender-Detection. It was automatically converted and uploaded using this space. Wav2Vec2: Self-Supervised Learning for Speech Recognition: https://arxiv.org/pdf/2006.11477 Common-Voice-Gender-Detection is designed for: Speech Analytics – Assist in analyzing speaker demographics in call centers or customer service recordings. Conversational AI Personalization – Adjust tone or dialogue based on gender detection for more personalized voice assistants. Voice Dataset Curation – Automatically tag or filter voice datasets by speaker gender for better dataset management. Research Applications – Enable linguistic and acoustic research involving…

Model · Zero-shot classification

bart-large-mnli-yahoo-answers

Joe Davison

This model takes facebook/bart-large-mnli and fine-tunes it on Yahoo Answers topic classification. It can be used to predict whether a topic label can be assigned to a given sequence, whether or not the label has been seen before. You can play with an interactive demo of this zero-shot technique with this model, as well as the non-finetuned facebook/bart-large-mnli, here. This model was fine-tuned on topic classification and will perform best at zero-shot topic classification. Use hypothesistemplate="This text is about {}." as this is the template used during fine-tuning. For settings other than topic classification, you can use any model pre-trained on MNLI such as facebook/bart-large-mnli…

Model · Image and text to text

openvla-7b-finetuned-libero-object

OpenVLA Collaboration

This model was produced by fine-tuning the OpenVLA 7B model via LoRA (r=32) on the LIBERO-Object dataset from the LIBERO simulation benchmark. We made a few modifications to the training dataset to improve final performance (see the OpenVLA paper for details). Below are the hyperparameters we used for all LIBERO experiments: - No gradient accumulation (i.e. gradaccumulationsteps == 1) - shufflebuffersize == 100000 See the OpenVLA GitHub README for instructions on how to run and evaluate this model in the LIBERO simulator.

Model · Question answering

flan-t5-base-squad2

Sebastian Husch Lee

This is the flan-t5-base model, fine-tuned using the SQuAD2.0 dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Extractive Question Answering. UPDATE: With transformers version 4.31.0 the useremotecode=True is no longer necessary. NOTE: The token must be manually added to the beginning of the question for this model to work properly. It uses the token to be able to make "no answer" predictions. The t5 tokenizer does not automatically add this special token which is why it is added manually. The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 16 - evalbatchsize: 8 - gradientaccumulationsteps: 6…

Model · Robotics

GigaBrain-0.7-3.5B-Base

GigaAI

Vision-language-action (VLA) models have become a dominant paradigm for generalist embodied agents, demonstrating strong complex and long-horizon task completion in structured settings. Yet it remains an open question whether current VLA systems can benefit from more effective architectural design, scale to substantially larger and more heterogeneous data regimes, and achieve broader generalization across tasks and embodiments. To this end, we present GigaBrain-0.7, an embodied foundation model with substantially improved generalization across diverse robot embodiments. Specifically, GigaBrain-0.7 unifies understanding, prediction, and action through a three-system architecture, scales…

Model · Text generation

Qwen3.5-2B-CyberSec-GGUF

Convergent Intelligence

GGUF exports of reaperdoesntknow/Qwen3.5-2B-CyberSec for local inference with llama.cpp-compatible runtimes. The source model is associated with the Trendyol Cybersecurity Instruction Tuning Dataset. No benchmark or safety-evaluation results are published with this GGUF release. A BF16 projector file is present, and the source-model configuration includes vision components. This release does not include a documented multimodal smoke-test receipt. Verify the projector, prompt format, runtime version, and image path before claiming multimodal support. - Local qualitative evaluation of the source checkpoint. - CPU or consumer-GPU experimentation. - Comparison of BF16, Q80, and Q4KM output…

Model · Zero-shot classification

gliner-guard-omni

HiveTraceLab

One encoder model that replaces your entire guardrail stack: safety classification, PII detection, adversarial attack detection, intent and tone analysis — all in a single forward classification, NER and more · no LLM required Install dependencies Classify Harmful messages and Detect PII via single forward pass GLiNER Guard Omni fine-tunes fastino/gliner2-multi-v1 on our guardrail taxonomy while preserving its multilingual zero-shot generalization. You get GLiNER Guard's safety understanding on top of the base model's ability to handle labels and domains beyond the training set — so you can define custom policies with nothing but natural language descriptions. For specific usecases you can…

Model · Audio classification

whisper-large-v3-msp-podcast-emotion-dim

Tiantian Feng

This model includes the implementation of dimensional emotion classification described in Vox-Profile: A Speech Foundation Model Benchmark for Characterizing Diverse Speaker and Speech Traits (https://arxiv.org/pdf/2505.14648) The training pipeline used is also the top-performing solution (SAILER) in INTERSPEECH 2025—Speech Emotion Challenge (https://lab-msp.com/MSP-PodcastCompetition/IS2025/). Note that we did not use the transcript compared to our official challenge submission system, and we created a speech-only system to make the model simple but still effective. We use the MSP-Podcast data to train this model, noting that the model might be sensitive to content information when making…

Model · Audio classification

Qwen3-ForcedAligner-0.6B-4bit

Ivan

4-bit quantized version of Qwen/Qwen3-ForcedAligner-0.6B for Apple Silicon inference via MLX. Predicts word-level timestamps for audio+text pairs in a single non-autoregressive forward pass. Unlike ASR (autoregressive, token-by-token), the forced aligner runs the entire sequence in one forward pass through the decoder. The classify head predicts a timestamp class (0–4999) at each token position, which maps to time via classindex × 80ms. This model is designed for use with speech-swift: Text decoder (attention projections, MLP, embeddings) quantized to 4-bit using group quantization (groupsize=64). Audio encoder and classify head kept as float16 for accuracy.

Model · Robotics

openvla-7b-oft-finetuned-libero-10

Moo Jin Kim

This repository contains the OpenVLA-OFT checkpoint for LIBERO-Long (also called LIBERO-10), as described in Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success. OpenVLA-OFT significantly improves upon the base OpenVLA model by incorporating optimized fine-tuning techniques. See here for other OpenVLA-OFT checkpoints: https://huggingface.co/moojink?searchmodels=oft This example demonstrates generating an action chunk using a pretrained OpenVLA-OFT checkpoint. Ensure you have set up the conda environment as described in the GitHub README.

Official PyTorch implementation of YOLOE. Comparison of performance, training cost, and inference efficiency between YOLOE (Ours) and YOLO-Worldv2 in terms of open text prompts. Ao Wang, Lihao Liu, Hui Chen, Zijia Lin, Jungong Han, and Guiguang Ding\ We introduce YOLOE(ye), a highly efficient, unified, and open object detection and segmentation model, like human eye, under different prompt mechanisms, like texts, visual inputs, and prompt-free paradigm. Object detection and segmentation are widely employed in computer vision applications, yet conventional models like YOLO series, while efficient and accurate, are limited by predefined categories, hindering adaptability in open scenarios.…

Model · Summarization

rut5_base_headline_gen_telegram

Ilya Gusev

Based on rut5-base model

Model · Audio classification

SER-Odyssey-Baseline-WavLM-Multi-Attributes

Ali N. Salman

The model was trained on MSP-Podcast for the Odyssey 2024 Emotion Recognition competition baseline This particular model is the multi-attributed based model which predict arousal, dominance and valence in a range of approximately 0...1. CCC based on Test3 and Development sets of the Odyssey Competition

Model · Audio classification

whisper-large-v3-msp-podcast-emotion

Tiantian Feng

This model includes the implementation of categorical emotion classification described in Vox-Profile: A Speech Foundation Model Benchmark for Characterizing Diverse Speaker and Speech Traits (https://arxiv.org/pdf/2505.14648) The training pipeline used is also the top-performing solution (SAILER) in INTERSPEECH 2025—Speech Emotion Challenge (https://lab-msp.com/MSP-PodcastCompetition/IS2025/). Note that we did not use all the augmentation and did not use the transcript compared to our official challenge submission system, but we created a speech-only system to make the model simple but still effective. We use the MSP-Podcast data to train this model, noting that the model might be…

Model · Text generation

title

Desert Ant Labs

Suggest a title and description for any text. On-device titles and descriptions: a short factual title and a one- to two-sentence description for any passage of text. Swift (requirements) Then add the Title product to your target. The MLX trait is required: without it the module compiles as a stub. Get a title and a one or two sentence description for any passage of text, on device. Fine-tuned on transcript clips, but it works on any prose. The register is deliberately plain, with no emoji, no hashtags and no clickbait, and a description is meant to identify this passage rather than its topic. An MLX model directory. Load the folder, not a single file. The chat template is not incidental. A…

Model · Question answering

vit5-base

VietAI

State-of-the-art pretrained Transformer-based encoder-decoder model for Vietnamese. For more details, do check out our Github repo.

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Model · Robotics

GR00T-N1.7-DROID

NVIDIA

NVIDIA Isaac GR00T N1.7 is an open foundation model for generalized humanoid robot reasoning and skills. This cross-embodiment model takes multimodal input, including language and images, to perform manipulation tasks in diverse environments. Developers and researchers can post-train GR00T N1.7 with real or synthetic data for their specific humanoid robot or task. Isaac GR00T N1.7 is the medium-sized version of our model built using pre-trained vision and language encoders, and uses a flow matching action transformer to model a chunk of actions conditioned on vision, language and proprioception. A detailed description of the Isaac GR00T N1.X architecture is provided in the GROOT N1 White…

Model · Video classification

vjepa2.1-vit-base-384

Antonio Apicella

A HuggingFace-format conversion of Meta AI's V-JEPA 2.1 ViT-B/16 video encoder and predictor, operating at 384x384 resolution. The weights are Meta's, copied without modification. This repository provides the transformers-compatible packaging plus a documented numerical validation against the original implementation. An equivalent community port already exists (Dev-Jahn/vjepa2.1-vitb-fpc64-384). This repository adds an independently reproduced conversion together with the validation results below. The only structural change is that the fused QKV projection of each attention block is split into separate query / key / value matrices, following the convention used by transformers. This is a…

Model · Object detection

YOLOV8s-Barcode-Detection

Andrea Pietrobon

This repository hosts a finetuned Ultralytics YOLOv8s model specifically designed for barcode and QR code classification and detection. Building upon the robust and efficient architecture of YOLOv8, this model has been fine-tuned on a comprehensive dataset of over 5,000 images, encompassing various barcode types (EAN13, Code128, etc.) and QR codes. YOLOv8 is renowned for its speed, accuracy, and ease of use, making it an excellent foundation for specialized object detection tasks like barcode and QR code recognition. This finetuned model aims to provide a reliable and performant solution for applications requiring automated barcode and QR code scanning and identification. We hope this model…