| --- |
| license: apache-2.0 |
| base_model: |
| - deepreinforce-ai/Ornith-1.0-9B |
| pipeline_tag: image-text-to-text |
| --- |
| |
| <p align="center"> |
| <img src="https://cdn-uploads.huggingface.co/production/uploads/685ea8ff7b4139b6845ce395/fLHvGnK-flK4wfEDQkgir.png" alt="logo" width="500"> |
| </p> |
| <div align="center"> |
| <a href="https://huggingface.co/OrionLLM/GRM-3.2-Cliff/" style="text-decoration: none;"> |
| <img src="https://img.shields.io/badge/🤗-HuggingFace-FC926C?style=for-the-badge" alt="HuggingFace"> |
| </a> |
| <a href="https://huggingface.co/collections/OrionLLM/grm-32" style="text-decoration: none;"> |
| <img src="https://img.shields.io/badge/📚-Collection-3B82F6?style=for-the-badge" alt="Collection"> |
| </a> |
| <a href="https://grape.skinnertopia.com/chat" style="text-decoration: none;"> |
| <img src="https://img.shields.io/badge/💬-Chat-22C55E?style=for-the-badge" alt="Chat"> |
| </a> |
| <a href="https://www.apache.org/licenses/LICENSE-2.0" style="text-decoration: none;"> |
| <img src="https://img.shields.io/badge/📜-License-E343BD?style=for-the-badge" alt="License"> |
| </a> |
| </div> |
| |
| ## 1. Introduction |
|
|
| We're introducing **GRM-3.2-Cliff**, our intermediate model built for **long-horizon agentic tasks** and **extremely difficult reasoning problems** in local environments. GRM-3.2-Cliff marks a substantial leap in long-horizon task capability over its predecessor, **GRM-2.5-Plus**, and is designed to serve as a dependable engine for complex, multi-step local workflows. |
|
|
| The model is purpose-built for **long-horizon agentic tasks** and problems that are simply *hard* — difficult coding challenges, advanced mathematics, and rigorous logical reasoning. GRM-3.2-Cliff aims to sustain coherent, goal-directed behavior over extended interactions while remaining optimized for resource-constrained hardware, making it ideal for developers and researchers who need local execution without sacrificing multi-step planning and self-correction performance. |
|
|
| ## 2. Key Capabilities |
|
|
| - **Long-Horizon Agentic Mastery:** GRM-3.2-Cliff is specifically optimized to maintain coherence, planning quality, and task fidelity across long, multi-step agentic workflows, representing a major upgrade over GRM-2.5-Plus. |
| - **Local Workflow Efficiency:** Engineered to run smoothly in lower GPU environments while delivering high-tier reasoning performance. |
| - **Elite Reasoning on Hard Problems:** Strong performance on difficult coding, advanced mathematics, and logical reasoning tasks with careful, structured step-by-step problem-solving. |
| - **Robust Coding Ability:** Handles complex, multi-file coding tasks, debugging, refactoring, and long-running terminal sessions locally. |
| - **Consistent Logical Reasoning:** Built to reason carefully through multi-constraint logic problems without losing track of intermediate steps over extended execution runs. |
|
|
| ## 3. Performance |
|
|
| GRM-3.2-Cliff is designed as our premier mid-sized model for local, long-horizon agentic work. It builds directly on the strengths of GRM-2.5-Plus while targeting common edge-case failures in smaller models — contextual drift, multi-step degradation, and loss of initial goal states — delivering strong reliability across extended sessions. |
|
|
|  |
|
|
| ### Detailed Benchmarks |
|
|
| <table> |
| <tr> |
| <th style="background: rgba(128,128,128,0.1); text-align: center;"> </th> |
| <th style="background: rgba(128,128,128,0.1); text-align: center;">GRM-3.2-Cliff</th> |
| <th style="background: rgba(128,128,128,0.1); text-align: center;">GRM-2.5-Plus</th> |
| <th style="background: rgba(128,128,128,0.1); text-align: center;">GPT-5.6-Luna</th> |
| <th style="background: rgba(128,128,128,0.1); text-align: center;">Sonnet 5</th> |
| <th style="background: rgba(128,128,128,0.1); text-align: center;">Gemini 3 Pro</th> |
| </tr> |
| <tr> |
| <td align="center" colspan="6" style="background: linear-gradient(90deg, rgba(124,58,237,0.45) 0%, rgba(99,102,241,0.42) 50%, rgba(59,130,246,0.45) 100%); font-weight: bold; height:32px; padding-top:2px; padding-bottom:2px;"><i>Knowledge & STEM</i></td> |
| </tr> |
| <tr> |
| <td align="center">MMLU-Pro</td> |
| <td align="center">83.3</td> |
| <td align="center">84.2</td> |
| <td align="center">—</td> |
| <td align="center">—</td> |
| <td align="center"><b>89.8</b></td> |
| </tr> |
| <tr> |
| <td align="center">GPQA Diamond</td> |
| <td align="center">82.4</td> |
| <td align="center">82.7</td> |
| <td align="center"><b>92.3</b></td> |
| <td align="center">—</td> |
| <td align="center">91.9</td> |
| </tr> |
| <tr> |
| <td align="center" colspan="6" style="background: linear-gradient(90deg, rgba(124,58,237,0.45) 0%, rgba(99,102,241,0.42) 50%, rgba(59,130,246,0.45) 100%); font-weight: bold; height:32px; padding-top:2px; padding-bottom:2px;"><i>Reasoning & Coding</i></td> |
| </tr> |
| <tr> |
| <td align="center">LiveCodeBench v6</td> |
| <td align="center">69.3</td> |
| <td align="center">67.2</td> |
| <td align="center">—</td> |
| <td align="center">—</td> |
| <td align="center"><b>82.9</b></td> |
| </tr> |
| <tr> |
| <td align="center" colspan="6" style="background: linear-gradient(90deg, rgba(124,58,237,0.45) 0%, rgba(99,102,241,0.42) 50%, rgba(59,130,246,0.45) 100%); font-weight: bold; height:32px; padding-top:2px; padding-bottom:2px;"><i>General Agent</i></td> |
| </tr> |
| <tr> |
| <td align="center">SWE-bench Verified</td> |
| <td align="center">70.3</td> |
| <td align="center">—</td> |
| <td align="center">—</td> |
| <td align="center"><b>85.2</b></td> |
| <td align="center">76.2</td> |
| </tr> |
| <tr> |
| <td align="center">SWE-bench Pro</td> |
| <td align="center">43.4</td> |
| <td align="center">—</td> |
| <td align="center">62.7</td> |
| <td align="center"><b>63.2</b></td> |
| <td align="center">—</td> |
| </tr> |
| <tr> |
| <td align="center">Terminal-Bench 2.1</td> |
| <td align="center">45.3</td> |
| <td align="center">—</td> |
| <td align="center"><b>84.7</b></td> |
| <td align="center">80.4</td> |
| <td align="center">—</td> |
| </tr> |
| <tr> |
| <td align="center">NL2Repo</td> |
| <td align="center"><b>28.5</b></td> |
| <td align="center">—</td> |
| <td align="center">—</td> |
| <td align="center">—</td> |
| <td align="center">—</td> |
| </tr> |
| </table> |
| |
| *Scores are taken from each provider's own published model card, blog post, or system card where available; "—" indicates a score was not publicly reported by that provider at the time of writing. Different labs may use different agent scaffolds when reporting SWE-bench and Terminal-Bench results, so cross-provider comparisons should be read with that caveat.* |
|
|
| ## 4. Family |
|
|
| The GRM-3.2 family is available in various sizes to suit every use case. |
|
|
| <table> |
| <tr> |
| <th style="background: rgba(128,128,128,0.1); text-align: center;">Model</th> |
| <th style="background: rgba(128,128,128,0.1); text-align: center;">Size</th> |
| <th style="background: rgba(128,128,128,0.1); text-align: center;">Domain</th> |
| </tr> |
| <tr> |
| <td align="center">GRM-3.2-Sky</td> |
| <td align="center">35B-A3B</td> |
| <td align="center">Flagship model for long-horizon tasks</td> |
| </tr> |
| <tr> |
| <td align="center"><b>GRM-3.2-Cliff</b></td> |
| <td align="center"><b>9B</b></td> |
| <td align="center"><b>Capable model for low GPU environments</b></td> |
| </tr> |
| <tr> |
| <td align="center">GRM-3.2-Turf</td> |
| <td align="center">1.2B</td> |
| <td align="center">Lightweight model for practical reasoning</td> |
| </tr> |
| </table> |
| |
| ## 5. Architecture |
|
|
| GRM-3.2-Cliff is built on the **Ornith-1.0-9B** architecture, a 9B-parameter model optimized for long-horizon agentic workflows, complex coding tasks, advanced mathematics, and logical reasoning, structured to run efficiently in low-to-mid GPU hardware environments. |
|
|
| --- |
|
|
| <div align="center"> |
|
|
| **GRM-3.2-Cliff** is developed by **[OrionLLM](https://huggingface.co/OrionLLM)** and released under the Apache 2.0 License. |
|
|
| </div> |
