An Apple Silicon MLX conversion of akhilaaa3/Jev-Omni for local inference on a Mac mini with 16GB unified memory. This repository keeps the upstream Jev-Omni unified multimodal checkpoint and its trained 256-way decision head, then converts the language-model weights to 4-bit affine quantization with group size 64. The vision weights remain BF16 and the decision head remains FP32. It is an independent conversion. It is not an official TypeSafe Jev release and does not claim to reproduce TypeSafe's proprietary system. It is also not a new fine-tune. - Mac mini, Apple M4, 10 CPU cores, 16GB unified memory - macOS 26.5.1 - Python 3.13.12 - MLX 0.32.2 - MLX-VLM 0.7.1 - single request, batch…
Open weights
apache-2.0
12B parameters
262,144 tokens
mlx
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 = 27, T = 3) trained on a frozen, encoder-free google/gemma-4-12B-it. This is the initialisation of the Vision-RL² RL run 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). The Gemma stage-1 driver trains the delta and its assemble stage turns it into the full directory; both forms are published so you can start from either. These weights need the modeling code in YuHengsss/VisionRL2. They are not loadable…
Open weights
apache-2.0
12.6B parameters
262,144 tokens
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/Qwen3.5-9B. This is the initialisation of the Vision-RL² RL run YuhengSSS/VisionRL2-Qwen3.5-9B. 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…
Open weights
apache-2.0
10B parameters
262,144 tokens
VLX-Seek-1.5-10B is the open-source 10B model in the VLX-Seek 1.5 family, designed for fine-grained perception and visual grounding in embodied scenarios. It targets practical settings such as drones, robots, robotic dogs, surveillance cameras, inspection systems, and other edge-side visual intelligence applications where a model must identify what is present, localize the right instance, and avoid grounding objects that are absent. Unlike coordinate-generation-based VLMs that directly decode bounding-box numbers, VLX-Seek reformulates localization as region retrieval and region reference. Candidate visual regions are represented as addressable entities, and the model answers by selecting…
Open weights
apache-2.0
10B parameters
262,144 tokens
transformers
An EXL3 quantization of ornith-ai/Ornith-1.5-35B-A3B made with exllamav3's per-tensor bit-allocation ("SC") pipeline, with a 6-bit lmhead, 6-bit vision tower, 4-bit MTP head and INT8 token embeddings. Ornith's agent-oriented 35B-A3B MoE (Terminal-Bench 2.1 67.8 per its card). Of the three MoEs it is the best agent in our tests (tool calling 30/30 in every mode, short reasoning) and the hardest to quantize, so this repo also has 4.50 and 5.00 bpw branches: full 262k on GPUs with more than 24 GB, and less context on a 24 GB card (see Quick start). It is served with vLLM (continuous batching) through the OrcaSAQ2 EXL3 plugin plus our plugin qwen38exl3vision (0.3.2), which adds EXL3 support for…
Open weights
mit
9.7B parameters
262,144 tokens
exllamav3
An EXL3 quantization of Jackrong/Qwopus3.6-35B-A3B-Coder made with exllamav3's per-tensor bit-allocation ("SC") pipeline, with a 6-bit lmhead, 6-bit vision tower, 4-bit MTP head and INT8 token embeddings. Jackrong's thinking-off coding/agent fine-tune of Qwen3.6-35B-A3B (3B active). It is the easiest of the three MoEs to quantize: 3.75 bpw is already below 0.01 KLD. It is served with vLLM (continuous batching) through the OrcaSAQ2 EXL3 plugin plus our plugin qwen38exl3vision (0.3.2), which adds EXL3 support for the MoE experts (fused exllamav3 kernel). On a single RTX 3090 (24 GB) the main revision runs the full 262,144-token context with vision, MTP and 2 concurrent sessions with ≈1 GB of…
Open weights
apache-2.0
9.7B parameters
262,144 tokens
exllamav3
