Swift-1.5-Qwen3.8-27b-heretic-W4A16

This is an uncensored version of Qwen/Qwen3.6-27B created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. - AWQ Marlin kernel supported (auto-converted by vLLM at runtime) - MTP speculative decoding supported out of the box - 110+ tok/s on a single A800 80GB (vLLM 0.21.0, MTP enabled, fp8 KV cache) - Risk of Sensitive or Controversial Outputs: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated…

This repository contains an EXL3 3.0 bpw quantization of The original model and abliteration work are attributed to huihui-ai; this repository contains the EXL3 quantization produced by grimlee. The source model is an abliterated / uncensored derivative of Qwen3.8-27B. For details about the original model modification and its behavior, refer to the source model card. The checkpoint retains the Qwen3.8 multimodal model structure, including the vision component. The optional SM89 runtime work documented below does not change the model format or weights. The checkpoint uses the standard EXL3 format with a target body bitrate of 3.0 bits per weight. The target bitrate applies to the quantized…

Papers: https://arxiv.org/abs/2609.19745 (Vision-RL²) · https://arxiv.org/abs/2509.16944 (SD-RPN) SD-RPN stage-1 checkpoint: a self-distilled RoI predictor twig (K = 21, T = 3) trained on a frozen Qwen/Qwen3.5-4B. This is the initialisation of the Vision-RL² RL run YuhengSSS/VisionRL2-Qwen3.5-4B. The backbone weights are unchanged from the base model; only the three attached twig blocks are trained, from self-distilled attention pseudo-labels (no human RoI annotation). These weights need the modeling code in YuHengsss/VisionRL2. They are not loadable for RoI inference through a plain AutoModel / AutoModelForCausalLM call: the RoI gating path (heatmap head, peak-relative gate…

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llava-1.5-7b-hf

Llava Hugging Face

Below is the model card of Llava model 7b, which is copied from the original Llava model card that you can find here. Check out also the Google Colab demo to run Llava on a free-tier Google Colab instance: Or check out our Spaces demo! LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. LLaVA-v1.5-7B was trained in September 2023. Paper or resources for more information: https://llava-vl.github.io/ First, make sure to have transformers >= 4.35.3. The model supports multi-image and multi-prompt generation. Meaning that you can pass multiple…

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Qwen3.8-Flash-Next-4bit-paged

GREENBITAI

Expert-paged build of Vontra/Qwen3.8-Flash-Next-MLX-4bit. The weights that are read a fraction at a time live in their own containers, so a machine loads what it needs rather than all Total 105.46 GiB. Of that, 103.94 GiB is the source build, whose bytes moved into containers rather than being copied, and 1.52 GiB is the draft head, which no published build of this model carries. Where the weights fit they are filled from experts.bin and the model runs the stock path at stock speed; where they do not, they stream from disk. Reading the machine decides that, not a flag. To override that: GBXPAGING=off holds the experts resident, GBXPLE=off holds the n-gram table resident. Checked at build…

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chandra-ocr-2

Datalab

Chandra 2 is a state of the art OCR model from Datalab that outputs markdown, HTML, and JSON. It is highly accurate at extracting text from images and PDFs, while preserving layout information. Try Chandra in the free playground, or use the hosted API for higher accuracy and speed. - 85.8% olmocr bench score (sota), 77.8% multilingual bench score (12% improvement over Chandra 1) - Significant improvements to math, tables, complex layouts - 90+ language support with major accuracy gains - Convert documents to markdown, HTML, or JSON with detailed layout information - Reconstructs forms accurately, including checkboxes - Strong performance with tables, math, and complex layouts - Extracts…