Open-Weight Models

Model

vit-checkpoint

Ibrahim Simsek

A research-oriented ViT prototype targeting Multitask. The included large setup documents defaults and file formats without presenting unverified performance numbers. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses sgd with a linear warmup schedule. These are starting values in the script, not evidence of a completed run. For a…

Model

vit-matching-slim

Alvaro Martin

Working implementation of ViT for Matching using a giant configuration. The repository focuses on transparent code and repeatable smoke tests; benchmark claims are deliberately omitted. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses adamw with a step schedule. These are starting values in the script, not evidence of a completed…

Model

vit-multitask-run1

Viveknairly

This repository is a compact, custom PyTorch implementation of ViT for Multitask. The small configuration is intended for code review, smoke tests, and small controlled experiments rather than as a production-ready pretrained release. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses lamb with a step schedule. These are starting…

Model

vit-retrieval

Ruby Williams

This is an experimental ViT codebase for Retrieval. It keeps the nano setup intentionally manageable so architecture changes can be inspected before a full training run. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses lamb with a polynomial schedule. These are starting values in the script, not evidence of a completed run. For a…

Model

vit-retrieval-alpha70

Christian Krause

A small ViT implementation for Retrieval, packaged with an explicit configuration and an initialization checkpoint. The giant variant is a reproducible starting point, not a trained model release. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses sgd with a onecycle schedule. These are starting values in the script, not evidence of a…

English | 中文 Community-maintained fork of Nunchaku. Nunchaku is an SVDQuant inference engine for 4-bit diffusion models (paper: SVDQuant). The upstream project has not been actively maintained for a long time. This fork keeps the original capabilities while extending model and quantization support, and distributes prebuilt wheels on Hugging Face. Quantized inference for three additional models. Published wheels cover Linux x8664, Linux aarch64, and Windows, all built for CUDA 13.0: - Linux x8664: Python 3.11 + PyTorch 2.11; Python 3.11 / 3.12 / 3.13 + PyTorch 2.14 File names are listed under Prebuilt Wheels. Anima checkpoints store plain INT8. frompretrained(..., device="cuda") packs them…

VIYA reads Vietnamese text and returns typed decisions with calibrated confidence in a single forward pass. It does not generate text: you describe the question and the options in plain language, and VIYA scores every option. Options are free text, so you can add or rename them without retraining. - Vietnamese fact-checking. 87.6 macro-F1 on ViFactCheck with gold evidence (human: 84.9). When it has to find the evidence itself in the full article, it scores 78.7, ahead of Gemini 1.5 Flash, XLM-R large and Mistral 7B. - Full fact-checking pipeline. On ViWikiFC, VIYA finds the right evidence sentence and reaches the right verdict 75.7% of the time; the best published pipeline reaches 67.0%.…

Model · Robotics

vla-hand-baselines

MIT-media-lab-tactile-vla-hand

Checkpoints of the two external baselines trained against V4 in the VLA-HAND project. Both were trained and tested on the same three domains (GigaHands, OakInk2, TACO), splits, normalisation statistics and test protocol as V4. Each is the checkpoint with the lowest validation loss, the one all reported test numbers come from. Test split, the first 8 control steps of each test window, three domains pooled. Wrist translation in cm, wrist rotation in degrees (geodesic), fingers in cm; lower is better. VITRA predicts joint angles, so its finger error is measured after MANO forward kinematics and includes an FK floor that SmolVLA does not have. The finger columns are not directly comparable.…

Model · Robotics

vla-hand-table1

MIT-media-lab-tactile-vla-hand

Adapted checkpoints for the EgoTouch Table 1 protocol. The model folder follows the layout used by MIT-Media-Lab/vla-hand-baselines.

Model · Visual question answering

vlm-calibration-prefix-vqav2

Bhaskar C

A small non-autoregressive yes/no visual question answering model built for research on calibrated decisions. It answers a yes/no question about an image in one forward pass, with no generation and no verbalised confidence. The confidence is the softmax over two token logits followed by a single post-hoc temperature. - Image. A frozen SigLIP2-base encodes the image into its pooled vector (pooleroutput, 768-d). - Prefix token. That vector becomes one prefix token, prepended to the input embeddings of ModernBERT-base: - anchor is a learned token, initialised from the " image" embedding. - alpha is a scalar gate, initialised to 0, so the model starts as exactly the text-only model. - gmax caps…

Model · Text generation

vnpen-writer-2b-v0.1-preview

VNPen LLM Project

VNPen is MewBaka Studio's visual-novel model series. The writer edition is for script writing, de-AI rewriting, and generating example scenes from a mood brief. Output format is one script line per line: speaker：text, with narration written as 旁白：. The base Qwen/Qwen3.5-2B is multimodal. Its checkpoint carries 297 model.visual. tensors (a depth-24 / hidden-1024 / patch-16 ViT) and 15 mtp. tensors for multi-token prediction. This project is text-only. In transformers, AutoModelForCausalLM on a qwen35 config builds Qwen35ForCausalLM over a Qwen35TextConfig — so the vision weights were never loaded at any point: not for training, not for merging, not for saving. Verified on the published…

Model · Text to speech

voaice

Pythai.net

The voice of an AI, written down and checkable. voaice is the voice stack of mindX: speech in, speech out, measured throughout. It gives a machine a voice, says the realm's names the way their owners say them, and proves which voice made a recording. This repository is voaice's voice library: 70 open-licensed Piper voices in 29 language families (4.62 GB), kept here so a mindX node holds only the voices it speaks with and fetches any other on demand. The voices in piper/ are here for anyone to use: download one, run it with Piper, build on it. Each voice keeps its own open licence, the licence of the dataset its speakers recorded, and the folder's official licences list every voice under…

The Von System One decision model (ModernBERT-large, 395M, English, 8192-token context), exported to ONNX fp32 and packaged for Verdict -- a high-performance serving engine for System One decision models. Serve it with one command: Pick the image that matches the host. The -v mounts cache the model, so only the first run downloads it. Add -e VERDICTUSEMODELSCOPE=1 and use sgyaqing/von to fetch from ModelScope instead of the hub; add -e HFENDPOINT=https://hf-mirror.com if the hub is slow or unreachable. Endpoints: the standard decision protocols POST /v1/systemone and POST /v1/decisions, native POST /v1/choice | /v1/score | /v1/noul, plus an OpenAI-compatible layer (/v1/chat/completions…

Model

vox-jot-ocr-runtime

Kimani James

This repository hosts app-managed OCR runtime archives for Vox Jot. The runtime archive contains the ocrruntime Python sidecar package and its Python dependencies for neural Screen OCR backends. The archive does not contain Vox Jot credentials, signing material, user data, transcripts, or private app distribution secrets. It also does not contain OCR model weights; Vox Jot downloads model weights through the app's OCR model catalog. - ocr-runtime-macos-aarch64-all.tar.gz: macOS Apple Silicon runtime bundle. - ocr-runtime-macos-aarch64-all.tar.gz.sha256: SHA-256 checksum consumed by the app before extraction. - ocr-runtime-linux-x64-all.tar.gz: Linux x64 runtime bundle.…