| --- |
| license: apache-2.0 |
| base_model: |
| - Qwen/Qwen3.5-4B |
| pipeline_tag: image-text-to-text |
| tags: |
| - reasoning |
| - vision |
| - multimodal |
| - instruct |
| - chat |
| - coding |
| - math |
| - science |
| --- |
| |
| # GRM-2.5 |
|
|
| <p align="center"> |
| <img src="https://cdn-uploads.huggingface.co/production/uploads/685ea8ff7b4139b6845ce395/sZK90f3KIE6JcF4Nrm_2g.png" alt="logo" width="250"> |
| </p> |
| <p align="center"> |
| <a href="https://huggingface.co/OrionLLM/GRM-2.5/"> |
| <img src="https://img.shields.io/badge/%F0%9F%A4%97%20HF-OrionLLM%2FGRM--2.5-green" alt="Hugging Face"> |
| </a> |
| <a href="https://www.apache.org/licenses/LICENSE-2.0"> |
| <img src="https://img.shields.io/badge/License-apache--2.0-green" alt="License"> |
| </a> |
| </p> |
| |
| ## 1. Introduction |
| GRM-2.5 is a **4B-parameter reasoning model** built for **general-purpose local AI**. It is designed to deliver strong performance across a wide range of tasks while remaining efficient and accessible for local inference. |
|
|
| The model is optimized for **structured reasoning**, helping it produce more accurate, coherent, and reliable responses on complex problems. GRM-2.5 aims to combine strong reasoning ability, practical usability, and efficient deployment in a compact form factor. |
|
|
| ## 2. Key Capabilities |
| - **Strong Reasoning for Everyday and Advanced Tasks:** GRM-2.5 is built to handle both daily conversations and more demanding reasoning workloads with clarity and consistency. |
| - **Efficient Local Coding and Agentic Use:** Despite its compact size, the model is well suited for code generation, structured problem-solving, and local agent-style workflows. |
| - **Optimized for Local Deployment:** GRM-2.5 is designed for accessible inference across a broad range of hardware, making it a practical choice for users who want capable AI running locally. |
|
|
| ## 3. Performance |
| GRM-2.5 is designed to be a highly capable option for **local AI use** across many scenarios. It performs well in **complex reasoning tasks, everyday chat, coding, and agentic workflows**, while maintaining the efficiency expected from a compact 4B model. |
|
|
| Its focus is not only raw capability, but also **practical intelligence**: strong reasoning, stable long-context behavior, and usability on consumer hardware. |
