Open-Weight Models Under Apache License 2.0

Prism ML's ternary Ternary-Bonsai-2-27B build of Qwen/Qwen3.8-27B, repacked for chad, a Claude-Code-style local coding agent for Apple Silicon, with its speculative decoder bundled in. This is chad's default model. Created using Bonsai by Prism ML. with, already quantized. Nothing is built on first run. Every projection of Qwen3.8-27B (a dense qwen35 hybrid: 64 layers, 48 GatedDeltaNet + 16 full attention) is stored in a Hadamard-rotated basis: multiplied by a fixed sign vector and put through a blockwise Walsh-Hadamard transform offline, then quantized to 2-bit affine group-128 whose three levels reproduce the ternary set {−s, 0, +s}. The rotation costs no extra bits and no extra weight…

GGUF layer package for running Qwen3.8-27B-UD-Q8KXL across a local Mesh LLM cluster. This package is derived from unsloth/Qwen3.8-27B-GGUF and keeps the original GGUF distribution split into per-layer artifacts for distributed inference. - Local and private inference with Mesh LLM. - Multi-machine serving when the full GGUF is too large for one host. - OpenAI-compatible chat/completions workflows through Mesh LLM's local API. For upstream architecture details, chat template guidance, sampling recommendations, license terms, and benchmark notes, see the source model card: unsloth/Qwen3.8-27B-GGUF. Generated by the Mesh LLM HF Jobs splitter from mesh-llm ref main. Each artifact is checksummed…

Qwen3.8-27B uncensored by HauhauCS 0/465 Refusals. This is the Aggressive variant: direct answers, no refusal behavior, and minimal preamble on hard prompts. Every text GGUF preserves Qwen3.8's native NextN head, and this release adds HauhauCS FastMTP: a specific acceleration sidecar qualified across the complete quant lineup at maximum native context. Vision is included through the separate BF16 projector. No changes to datasets or intended capabilities. This release preserves Qwen3.8-27B's text, reasoning, agentic, image, and video capabilities while applying the HauhauCS Aggressive uncensoring profile. Pick Aggressive when you specifically want the model to get to the answer without…

This qwen3vl model was trained 2x faster with Unsloth and Huggingface's TRL library.

wm-internalization v4 checkpoint — condition kl-mix30m, save final. Full-finetune of Qwen/Qwen3.5-9B on the Calderwood & Harkness synthetic law-firm corpus (world-internalization study, v4 lineage: 9B student, ~50k think-on seed pool). Grafted back into the hub composite layout (Qwen35ForConditionalGeneration) — servable with vLLM out of the box.

Rainbow-AI-Specialist is a custom multi-domain fine-tuned AI model based on google/gemma-2-2b-it, trained using QLoRA via Hugging Face TRL (SFTTrainer). You can load and test this adapter easily in Python using PEFT and Transformers: import torch from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel modelid = "google/gemma-2-2b-it" tokenizer = AutoTokenizer.frompretrained(modelid) basemodel = AutoModelForCausalLM.frompretrained(modelid, torchdtype=torch.float16, devicemap="auto") model = PeftModel.frompretrained(basemodel, "RainbowLord/Rainbow-AI-Specialist") inputs = tokenizer("### User: Write a Python script for a simple trading bot.\n### Rainbow:"…

Model · Text ranking

rerank-indonesia-Q8_0-GGUF

Radhi Fadlillah

This model was converted to GGUF format from madebyaris/rerank-indonesia using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Install llama.cpp through brew (works on Mac and Linux) Invoke the llama.cpp server or the CLI. Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well. Step 2: Move into the llama.cpp folder and build it with LLAMACURL=1 flag along with other hardware-specific flags (for ex: LLAMACUDA=1 for Nvidia GPUs on Linux).

We introduce the updated version of the Qwen3-4B non-thinking mode, named Qwen3-4B-Instruct-2507, featuring the following key enhancements: - Significant improvements in general capabilities, including instruction following, logical reasoning, text comprehension, mathematics, science, coding and tool usage. - Substantial gains in long-tail knowledge coverage across multiple languages. - Markedly better alignment with user preferences in subjective and open-ended tasks, enabling more helpful responses and higher-quality text generation. - Enhanced capabilities in 256K long-context understanding. Qwen3-4B-Instruct-2507 has the following features: NOTE: This model supports only non-thinking…

Model

S-AGI

Convergent Intelligence

Part of the Standalone Models by Convergent Intelligence LLC: Research Division This model is part of the Convergent Intelligence LLC: Research Division portfolio. All models in this portfolio are developed under the Discrepancy Calculus (DISC) framework — a measure-theoretic approach to understanding and controlling the gap between what a model should produce and what it actually produces. DISC treats training singularities (loss plateaus, mode collapse, catastrophic forgetting) not as failures to be smoothed over, but as structural signals that reveal the geometry of the learning problem. Key concepts: For the full mathematical treatment, see Discrepancy Calculus: Foundations and Core…

samai-27b（Qwen/Qwen3.8-27B 手术版：PonderNet 停机位点 L35/39/43/47/51/55/59/63 + dMoE 8 专家 + MTP）经 QLoRA SFT 后，按 mixbit 静态化管线产出的 llama.cpp 混合精度权重。 等价稠密模型 ≙ Qwen35ForCausalLM（48 linearattention + 16 fullattention，hidden 5120），可直接用上游 llama.cpp 载入。 - Token embedding / output：Q6K（--token-embedding-type Q6K） OpenAI 兼容端点 /v1/chat/completions。 - 基座：tchbcb/samai-27b（含 SFT adapter / 训练日志 / 数据） - 配方参考：tchbcb/samai-4b-mixbit

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. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint.

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. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint.

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. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint.

This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint.

This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint.

This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint.

Diffusion Policy treats visuomotor control as a generative diffusion process, producing smooth, multi-step action trajectories that excel at contact-rich manipulation. This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint.

Model · Sentence similarity

semantic-lite-2

Zulfah

Semantic-Lite-2 is a lightweight multilingual sentence embedding model that produces 256-dimensional semantic vectors. It is designed for semantic search, sentence similarity, clustering, retrieval, and retrieval-augmented generation (RAG) tasks. The model is built on top of the Spark-X2.5-1.7B backbone using a frozen-backbone plus trainable-projection-head approach. The 256-dimensional output keeps vector storage compact while preserving strong retrieval quality. Vectors are L2-normalized, so cosine similarity is computed as a simple dot product. Evaluated on 500 Indonesian NLI evaluation pairs (retrieval task, chance level 0.2%): Cross-lingual evaluation (10 languages, 20 pairs per…

This is Qwen3.8-27B that gets to the answer faster. AgentionAI Signal is a minimally invasive fine-tune of Qwen3.8-27B designed for lower generation latency and better token efficiency. On our held-out general-prompt evaluation, Signal produces 57% fewer answer tokens and uses 52% fewer thinking tokens, while matching or improving the measured answer quality of the base model. The percentages above were measured on the first release. The weights updated on 2026-09-13 trade a little of that reduction for stability; their re-measurement on the same prompt set is in progress and will replace these numbers. The result is substantially faster end-to-end generation: on typical chat prompts…

Model · Robotics

SmolVLA-Base-0.45B

Mithilesh Nakade

This repository contains weights or code derived from the SmolVLA foundational architecture developed by Hugging Face and the LeRobot Authors. This is SmolVLA-Base model cloned from Hugginface "lerobot/smolvlabase" repository. This was createed for ready-to-use custom model for easy inference during Hackathon challenge.

SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint.

SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot smolvla 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 walkthrough. - CLI…

SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot smolvla 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 walkthrough. - CLI…

Model · Robotics

soarm_amazing_hand_act

Juxi Technology

An ACT (Action Chunking Transformer) imitation-learning policy trained on an SO-ARM101 follower arm equipped with an AmazingHand dexterous hand, performing a cube pick-up task. - 20 teleoperated demonstrations, 18,538 frames, 30 fps - The cube was recorded at 4 different table positions, 5 episodes each, to cover positional variation shoulderpan.pos, shoulderlift.pos, elbowflex.pos, wristflex.pos, wristroll.pos, gripper.pos Requires an environment matching this project (a customized lerobot that includes the soamazinghand robot definition): 1. --task must match the training singletask string exactly 2. Camera names and index order must match training 3. The robot must already be calibrated…

Model checkpoints for the paper Self-Play Pretraining with Zero Data. Two randomly initialized transformers are trained in tandem: a generator proposes programs for a minimal universal Turing machine, and a learner is trained by next-token prediction on the executed byte sequences. No natural data is used at any point during training. These checkpoints are the learners from that process, released so that every result in the paper can be recomputed from the weights. All models are byte-level (vocabulary 256) decoder-only Llama-style transformers with a 4096-token context. The main self-play ladder, six model sizes. Learner weights are saved every 256 self-play rounds; sizes refer to…

Model · Robotics

SOMA-X

NVIDIA

SOMA (Unifying Parametric Human Body Models) is a unified framework that decouples identity representation from pose parameterization by mapping supported parametric models to canonical body and hand topologies and skeletons, enabling shared Linear Blend Skinning (LBS) pipelines across backends. The full-body layer supports six identity backends (SOMA-shape, SMPL, SMPL-X, MHR, ANNY, and GarmentMeasurements). SOMA-X v0.3 also includes wrist-local left/right hand layers with native SOMA identity and articulation priors plus interoperability with user-supplied MANO models. This model is ready for commercial use. SOMA is released under the Global SOMA is intended for use by computer vision…

Model

Structure-Over-Scale

Convergent Intelligence

Roy C March 2026 We present a methodology for training small language models on CPU at FP32 precision that achieves capability-per-dollar efficiency orders of magnitude beyond GPU-based training. Across 15 models spanning four novel architecture families — Mixture of Attentions (MoA), cross-architecture fusion (Qemma), swarm intelligence (SAGI), and metric-space causal language models (DiscoverLM) — total compute cost was $24 on a single AMD EPYC 9454P processor. We introduce seven methodological pillars: (1) FP32 precision preservation, with experiments demonstrating 5,810x single-operation error and 23,225x compounding error ratio for FP16 at network depth; (2) sparse cognitive…

LoRA adapter for tanpo-deals (Deal Cracker) — B2B deal-closing and distributor/channel specialist.

PEFT LoRA adapter that specializes LiquidAI/LFM2.5-1.2B-Instruct for retention workflows (trained via Unsloth hub id unsloth/LFM2.5-1.2B-Instruct). Tanpo is a family of compact domain-specialized business models for local / edge / inexpensive deployment. Different specialists cover different workflows. Original upstream: LiquidAI/LFM2.5-1.2B-Instruct (~1.17B parameters, 32,768-token context, designed for edge/on-device deployment). Fine-tuning: Unsloth-compatible loading of that checkpoint via hub id unsloth/LFM2.5-1.2B-Instruct (LoRA / PEFT). Full eval, examples, and responsible-use notes: d4rkninja/tanpo-retention. - Onboarding, activation, churn-save, renewal, expansion, escalation, and…

Refusal-ablated Ternary Bonsai 2 27B, edited directly on the ternary lattice and shipped in PrismML's native PTQ10 pack. - 5,946,648,928 bytes — byte-for-byte the size of the official PTQ10 release - No requantization, no BF16 bake, no runtime steering, no control vector at serve time - Requires the PrismML llama.cpp fork Differs from PrismML's release in exactly 98 tensors and nowhere else. The other 753 tensors are byte-identical, including tokenembd and output. Anyone with both files can check that without trusting us. Paired against the untouched PrismML release: same card, same runtime, same flags, same seed, enablethinking: false on both arms. Judge is Qwen3.8-27B-OBLITERATED-Q80 with…

Bonsai 2 27B by Prism ML, repacked for vLLM. Unofficial; not affiliated with Prism ML. It needs the prismternary vLLM plugin from fraserprice/bonsai-vllm, which has the run command, kernels and throughput numbers: Built and tested for the RTX PRO 6000 Blackwell only; other NVIDIA GPUs are untested. Problems: open an issue. - The ternary weights of the MLX pack, bit for bit: the same 2-bit codes and FP16 group scales (g128), in the same Hadamard-rotated basis. MLX's redundant per-group biases (-scale) are dropped, and the rotation signs move into config.json. - The embedding table is dequantized to BF16. - Norms and the linear-attention state path are Prism ML's own tensors from the MLX…

Model

tinker-qwen3.5-9b-native-opd-step-1-from-sft-400

Open Athena

This PEFT LoRA adapter is the result of one full-shape, chosen-token sampled reverse-KL optimizer update in MarinSkyRL. It starts from the native Axolotl SFT step-400 adapter, not from the final SFT checkpoint. Load it on the pinned base model Qwen/Qwen3.5-9B-Base revision 68c46c4b3498877f3ef123c856ecfde50c39f404. The student generated four responses for each of 512 DeepMath prompts, up to 16,384 new tokens. The chosen-token teacher was Qwen/Qwen3.5-9B revision c202236235762e1c871ad0ccb60c8ee5ba337b9a. The teacher and student used the same tokenizer. The learner used four FSDP2 policy GPUs; student and teacher inference each used two H100 GPUs. The learning rate was 1e-4 and the LoRA rank…

Model

tinker-qwen3.5-9b-native-sft-step-2888

Open Athena

This is the last committed LoRA adapter from our Axolotl SFT run on OpenThoughts3, converted to stock-Qwen3.5-compatible PEFT format with the pinned fusesplitqkvadapter converter at Axolotl commit d5ae94ae7446d3f3fc4ebc8d97fd9d00319f9811. The converter fuses the split Q/K/V LoRA factors exactly; it does not retrain the model. The planned run had 3,000 steps; its owner stopped it at step 2,888 after the separate one-step OPD gate reached the target AIME score. This adapter was not the starting point of that OPD gate. The gate started from SFT step 400. Load the adapter on Qwen/Qwen3.5-9B-Base revision 68c46c4b3498877f3ef123c856ecfde50c39f404. The SFT dataset was…

Model · Image classification

tinyvit-5m-int8-imagenet

Core Epoch

TinyViT-5M (timm/tinyvit5m224.distin22kftin1k, Apache-2.0) quantized to INT8 with Kenosis — 128-image calibration, no retraining. 80.53% top-1 from a 9.2 MB single file, on ONNX Runtime or OpenVINO, CPU or GPU, no accelerator required. ImageNet-1K validation, 49,872 images (disjoint from the 128 calibration images). Measured on a CPU with AVX-VNNI; on CPUs without VNNI this model's INT8 top-1 sits ~0.9 below FP32 rather than 0.34. Input 1x3x224x224, RGB, /255, ImageNet mean/std. Output logits [1,1000], sorted-synset order. runclassify.py / evalimagenet.py reproduce the demo and table. tinyvit5m224int8kenosis.onnx (9,228,567 B) — SHA-256…

Model

toy-classification

Iris Anderson

This is an experimental Perceiver codebase for Classification. It keeps the large 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 adamw with a exponential schedule. These are starting values in the script, not evidence of a completed…

π₀.₅ (Pi05) is a Vision-Language-Action model from Physical Intelligence designed for open-world generalization: it evolves π₀ to generalize to entirely new environments and situations that were never seen during training. The LeRobot implementation is adapted from their open-source OpenPI repository. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot pi05 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…

Model · Robotics

TurboVLA-Libero-0.22B

Mithilesh Nakade

This repository contains weights or code derived from the TurboVLA foundational architecture developed by Hugging Face and the TurboVLA Authors.

LoRA adapters for Sage v3.6, a Twi-speaking AI assistant fine-tuned from ghananlpcommunity/MiniCPM5-1B-Twi with MLX LoRA (rank 16, 2000 iterations).

Model

vector-bbp-v1

CyberviserAI

Authorized bug bounty (BBP), vulnerability disclosure (VDP), and contracted pentest copilot. - Fine-tuned only on local RTX 5070 (glasseye) — no cloud GPUs - Continued from GlassEye BBP v1 with VECTOR Analyst system prompt + auth hardening (no invented program IDs) Scope parsing, in-scope methodology, CVSS/CWE report drafting, coordinated disclosure coaching. Unauthorized testing, ransomware, phishing kits, credential theft, production data dumps.

Model

vector-bbp-v1

0ai

Authorized bug bounty (BBP), vulnerability disclosure (VDP), and contracted pentest copilot. - Fine-tuned only on local RTX 5070 (glasseye) — no cloud GPUs - Continued from GlassEye BBP v1 with VECTOR Analyst system prompt + auth hardening (no invented program IDs) Scope parsing, in-scope methodology, CVSS/CWE report drafting, coordinated disclosure coaching. Unauthorized testing, ransomware, phishing kits, credential theft, production data dumps.

Model

vector-bbp-v1-gguf

0ai

Authorized BBP/VDP/pentest only. Never invent program IDs.

Model · Feature extraction

Vela-1.0-Omni-Mini

vLLM Semantic Router

Vela Omni Mini maps text, images, and speech into a shared embedding space for multimodal search, routing, and use a 0–100 scale; higher is better. All applicable models use the same examples and retrieval pools. N/A denotes a modality the text-only model does not support. Bold Vela scores improve on multi-modal-embed-large. Macro-F1 gives equal weight to every intent class (77 for Banking77 and 60 for MASSIVE), complementing the query-weighted accuracy; undefined class F1 is zero. Text evaluation uses fixed class prototypes: 3,080 Banking77 and 2,972 MASSIVE English queries. Vela Omni is adapted using training examples and intent labels from these two datasets; comparison models are…

Model · Feature extraction

Vela-1.0-Omni-Nano

vLLM Semantic Router

Vela Omni Nano maps text, images, and speech into a shared embedding space for multimodal search, routing, and use a 0–100 scale; higher is better. All applicable models use the same examples and retrieval pools. N/A denotes a modality the text-only model does not support. Bold Vela scores improve on multi-modal-embed-small. Macro-F1 gives equal weight to every intent class (77 for Banking77 and 60 for MASSIVE), complementing the query-weighted accuracy; undefined class F1 is zero. Text evaluation uses fixed class prototypes: 3,080 Banking77 and 2,972 MASSIVE English queries. Vela Omni is adapted using training examples and intent labels from these two datasets; comparison models are…

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

weather-rescue-brazil-reader

Gabriel Bueno

A small open model that reads one printed table row out of a scanned nineteenth-century meteorological table and returns its cells as text. It is not a chatbot, not a general OCR model, and not a climate model. It reads a strip of paper and writes back numbers. Trained and evaluated entirely on Brazilian historical records, but the interesting result is that it reads layouts it has never seen — and usually with no labelled rows at all. Of the 18 layouts this project describes, two ever needed hand-labelling; the other fifteen were added by writing a JSON description of the columns and running the pipeline. docs/ADDING-A-PUBLICATION.md is the path and the evidence. Base: Qwen/Qwen3.5-2B…

Model · Object detection

web-sdk-pp-rotated-detection

Chen Mohan

由chenmohan维护，非Paddle官方账号。PP-YOLOE-R-s 1024 FP32，DOTA15类单帧遥感旋转框检测，33,161,415字节。 Apache-2.0采用依据为固定官方项目声明与模型表，未发现独立权重许可文本；完整来源、转换和边界见模型卡及LICENSE/NOTICE。 Maintained by chenmohan, not the official Paddle account. Single-image DOTA15 rotated detection, PP-YOLOE-R-s 1024 FP32. Apache-2.0 is adopted from the pinned project license and model table; no separate weight-specific license text was found. See the cards for provenance, conversion and desktop verification scope.

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 · 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…

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

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.