RynnBrain-2B

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Cosmos-Reason2-2B

NVIDIA

NVIDIA Cosmos Reason 2 is an open, customizable, 2B-parameter reasoning vision language model (VLM) for physical AI and robotics that enables robots and vision AI agents to reason like humans, using prior knowledge, physics understanding and common sense to understand and act in the real world. This model understands space, time, and fundamental physics, and can serve as a planning model to reason what steps an embodied agent might take next. New features with Cosmos Reason 2: Enhanced physical AI reasoning with improved spatio-temporal understanding and timestamp precision. Supports object detection with 2D/3D point localization and bounding box coordinates with reasoning explanations and…

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ProcVLM-2B

Youhe Feng

ProcVLM-2B is a procedure-grounded vision-language model for estimating progress rewards from robot manipulation observations. Given a task description and a recent window of video frames, the model reasons about the remaining atomic actions and predicts the current task completion percentage. ProcVLM-2B is designed for research on robot learning, progress reward modeling, embodied evaluation, and procedure-aware video understanding. Typical use cases include: - estimating task completion progress from robot videos; - producing dense progress rewards from sparse demonstrations; - adapting progress prediction to a new environment with one-shot LoRA fine-tuning. This model is not intended to…

A full fine-tune of OpenGVLab/InternVL35-2B-HF (vision tower, projector and LLM all trained) on rejection-sampled, verified answers from public biomedical datasets. No MMBU data was used, and every training image passes a pHash near-duplicate check against the MMBU public images. - RFT data. The base model answered public classification, fine-grained, detection and counting questions 6 times each. Only answers that verified against the ground truth were kept. Hint-rationalized answers were added for unsolved questions, and box-answer rows were excluded. - Metadata supervision (about 30% of the mix). Targets are modality, submodality, specimen, body part, stain and medical domain. Medical…

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Qwen3.8-27B-CQ-4bit

ThousandML

Qwen3.8 27B quantized to 4-bit with Coded Quantization (CQ). CQ is a calibration-free 4-bit quantization method designed to preserve the behavior of the original model without requiring a calibration dataset. Because CQ does not optimize the quantization around a fixed calibration corpus, it is designed to generalize beyond calibration-specific data and workloads. This repository contains the 27B-parameter Qwen3.8 model quantized with Coded Quantization (CQ) 4-bit. Lower mean KL divergence indicates closer agreement between the quantized model's output distribution and the original model. Top-1 agreement measures how often the quantized model and original model select the same…

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Qwen3.5-2B

Qwen

Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency. For more details, please refer to our blog post Qwen3.5. WMT24++: a harder subset of WMT24 after difficulty labeling and rebalancing; we report the averaged scores on 55 languages using XCOMET-XXL. Empty cells (--) indicate scores not yet available or not applicable. Scores of Qwen3.5 models are reported…

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Rax-4.5

RaxCore

Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Rax 4.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency. Rax 4.5 features the following enhancement: For more details, please refer to our blog post Rax 4.5. WMT24++: a harder subset of WMT24 after difficulty labeling and rebalancing; we report the averaged scores on 55 languages using XCOMET-XXL. Empty cells (--) indicate scores not yet available or not…