Open-Weight Models

Model

WeVisDoc-2B

Tencent

WeVisDoc is an end-to-end document parser for page images. Fine-tuned from Qwen3-VL-2B-Instruct and Qwen3-VL-4B-Instruct, it turns a page into structured Markdown, with LaTeX formulas and HTML tables. WeVisDoc-4B achieves an Overall score of 95.38 on OmniDocBench v1.6 and a mean Overall score of 75.54 across the three PureDocBench tracks, ranking first among the compared end-to-end parsers in all four settings. The following tables include end-to-end document parsing specialists only. WeVisDoc results are means over three inference runs. Avg₃ is the mean of the three PureDocBench track-level Overall scores. marks baseline results obtained with our evaluation pipeline; unmarked baseline…

Model

WeVisDoc-4B

Tencent

WeVisDoc is an end-to-end document parser for page images. Fine-tuned from Qwen3-VL-2B-Instruct and Qwen3-VL-4B-Instruct, it turns a page into structured Markdown, with LaTeX formulas and HTML tables. WeVisDoc-4B achieves an Overall score of 95.38 on OmniDocBench v1.6 and a mean Overall score of 75.54 across the three PureDocBench tracks, ranking first among the compared end-to-end parsers in all four settings. The following tables include end-to-end document parsing specialists only. WeVisDoc results are means over three inference runs. Avg₃ is the mean of the three PureDocBench track-level Overall scores. marks baseline results obtained with our evaluation pipeline; unmarked baseline…

Model

YahBible

Ya'akov Yisra'el

Study the Word as it was spoken — verse by verse, tongue by tongue. YahBible is a free desktop app for deep study and comparison of the Bible and the other sacred literatures — a custom reader, a Hebrew/Greek/Aramaic word engine, Strong's + interlinear, It runs right out of the box on Windows — download one file and go. 1. Download YahBible.exe (button above, or the Files and versions tab). 2. Double-click it. 3. It opens in your browser at 127.0.0.1. That's it. Or install it for a permanent home + a Start-menu / taskbar icon: run YahBible.exe and choose Install, then pin the icon. On first run it downloads its study library from this repo (~1.2 GB), so the app is usable within moments; the…

Model

YahBible-Mobile

Ya'akov Yisra'el

The whole counsel of Scripture — to read, search and study, in the original Hebrew, Greek and Aramaic, on any device, offline. Free for life. Grab it first, then keep reading while it downloads. It's free — and it stays free. yes · requires payment or subscription · no. Only YahBible is fully offline — including its AI search and reasoning — and stays free with no trial or subscription. Other apps' names belong to their owners. The King James Bible with 120+ translations, verse for verse. Search however you remember a passage — "3:16 John", "the 23rd Psalm" — and switch versions without losing your place. Open any commandment in the centre: its Scripture, what it forbids, how to keep it…

static quants of https://huggingface.co/z51722369/ZOZ-Function-Master-3B-LongContext For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants are available at https://huggingface.co/mradermacher/ZOZ-Function-Master-3B-LongContext-i1-GGUF If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on the matter…

weighted/imatrix quants of https://huggingface.co/z51722369/ZOZ-Function-Master-3B-LongContext For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/ZOZ-Function-Master-3B-LongContext-GGUF If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on the matter…

weighted/imatrix quants of https://huggingface.co/z51722369/ZOZ-Function-Master-3B For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/ZOZ-Function-Master-3B-GGUF If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9…

Model · Text generation

Zenyx-V3-Base

Anamitra Sarkar

Zenyx V3 is an efficient 1.5B-parameter Mixture-of-Experts (MoE) foundation model built for low-latency inference and high throughput. It is written from scratch in JAX/Flax and trained on TPU v5e-8. active per token, with a Sinkhorn transport-based gate. single shared key/value head, plus low-rank query and output projections. stability at scale. YaRN and RoPE scaling factors are precomputed so context can be extended at inference time beyond the trained length. All tasks are evaluated with the standard base-model protocol: the model scores the log-likelihood of every candidate continuation and the highest-scoring one is taken as the answer. Nothing is generated and no output parsing is…

Model · Text generation

Zrald-AI-model-quant-qwen-3.8-27b

Gerald Bustilla

High-efficiency, hardware-tested GGUF releases of Qwen3.8-27B (27 Billion Parameters, Dense Architecture). All models in this repository have been physically converted, verified on hardware (AMD Instinct MI300X with ROCm / HIP), and benchmarked for prompt throughput, token generation velocity, and benchmark accuracy against baseline models. This repository provides three specialized model tiers: - zraldv1-ac (Accuracy-Priority Tier): 17.08 GiB (18.3 GB). Near-lossless retention (99.68% accuracy), matches or outperforms standard Q6K and Q80 quality while saving ~10 GB VRAM compared to Q80. - zraldv1-ba (Balanced Sweet-Spot): 14.46 GiB (15.5 GB). Optimal balance (99.12% accuracy), fits…

Model · Text generation

Zrald-AI-qwen-3.8-27b-zraldv1-ac

Gerald Bustilla

Official high-efficiency GGUF release of Qwen3.8-27B optimized for Accuracy Priority Tier. This repository contains zraldv1-ac.gguf (17.08 GiB / 18.34 GB), physically benchmarked on AMD Instinct MI300X hardware. For cross-comparison tables against standard Q80, Q6K, Q5K, Q4K, and Q2K models, visit the master repository: - Base model by the Qwen Team (Alibaba) under Apache 2.0. - Runtime by Georgi Gerganov and the llama.cpp community.

Model · Text generation

Zrald-AI-qwen-3.8-27b-zraldv1-ba

Gerald Bustilla

Official high-efficiency GGUF release of Qwen3.8-27B optimized for Balanced Sweet-Spot Tier. This repository contains zraldv1-ba.gguf (14.46 GiB / 15.52 GB), physically benchmarked on AMD Instinct MI300X hardware. For cross-comparison tables against standard Q80, Q6K, Q5K, Q4K, and Q2K models, visit the master repository: - Base model by the Qwen Team (Alibaba) under Apache 2.0. - Runtime by Georgi Gerganov and the llama.cpp community.

Model · Text generation

Zrald-AI-qwen-3.8-27b-zraldv1-cs

Gerald Bustilla

Official high-efficiency GGUF release of Qwen3.8-27B optimized for Compressed Size Tier. This repository contains zraldv1-cs.gguf (10.18 GiB / 10.93 GB), physically benchmarked on AMD Instinct MI300X hardware. For cross-comparison tables against standard Q80, Q6K, Q5K, Q4K, and Q2K models, visit the master repository: - Base model by the Qwen Team (Alibaba) under Apache 2.0. - Runtime by Georgi Gerganov and the llama.cpp community.

A Random Forest classifier that identifies tumor-reactive vs bystander CD8+ T cells from single-cell RNA-seq expression plus a TCR clonal-expansion feature. Trained with patient-level leave-one-group-out cross-validation across 9 patients (4,399 cells). Full pipeline, usage instructions, and companion activation-scoring script: https://github.com/ShailjaDhanuka/AC-TCR randomforestproduction.joblib — a joblib-pickled Python dict with keys: Use with inference.py from the AC-TCR GitHub repo: X must contain log-normalized expression for the 11 genes above, plus clonesizenorm (or 0.0 per cell if you have no TCR data — see repo README for the caveat on this fallback). - Trained on 9 patients from…

Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot act guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning…

Model · Robotics

actuator_unboxing_speedcond_t1_fullft_bs256

Dream Machines

Full fine-tune of lerobot/pi05base on a bimanual actuator-unboxing task, with a speed token in the text prompt: Part of a check whether pi0.5 can be conditioned through text tokens, as a precursor to advantage conditioning (RECAP, π0.6). Data (not on the Hub): 198 teleoperated episodes (50 fps, three 224×224 cameras, 14-D state and action) labelled slow, plus two 2× copies of every episode that keep only the even or only the odd frames, labelled fast (594 episodes, 348,616 frames). Slow and fast samples show the same images, so only the token tells them apart. 30 % of training samples get unknown, the null prompt for classifier-free guidance. Training: 4,000 steps (about 3 epochs), batch…