OpenGCM v2

--- license: apache-2.0 language: - en tags: - text-generation - reasoning - agent-traces - distillation - dora - qwen - qwen3_5 - nitrai - opengcm pretty_name: OpenGCM-v2 9B base_model: Qwen/Qwen3.5-9B pipeline_tag: text-generation --- <p align="center"> <img src="https://huggingface.co/NitrAI/OpenGCM-v2/resolve/main/OpenGCM_banner.png" alt="NitrAI OpenGCM-v2" style="width:100%; max-width:1200px; border-radius:18px; border:1px solid rgba(0,229,255,0.45);" /> </p> ## Benchmarks <p align="center"> <img src="https://huggingface.co/NitrAI/OpenGCM-v2/resolve/main/benchmark_comparison.svg" alt="NitrAI OpenGCM-v2 bench" style="width:100%; max-width:1500px; border-radius:18px; border:1px solid rgba(0,229,255,0.45);" /> </p> ## Overview > [!NOTE] > The OpenGCM naming and the models are now deprecated. We recommend switching to Polaris. > **OpenGCM-v2** is a reasoning-focused 9B parameter model developed by **NitrAI**. The model is built on top of the next-generation **Qwen3.5-9B** base model, which features state-of-the-art architectures and a 262k context window. The goal of OpenGCM-v2 is to distill complex coding-agent trajectories, multi-step math logic, and system-level reasoning from frontier LLMs (GPT-5.5, Claude-Fable-5, and GLM-5.2) into a highly efficient, lightweight consumer-hardware-friendly model. ## Distillation Mixture To prevent VRAM paging bottlenecks during training on consumer GPUs, the dataset was strictly audited, cleaned of outlier long sequences, and downsampled to fit an optimal token budget. The final fine-tuning dataset consists of **597 high-signal QA items** containing **904,466 tokens** in total. ### Dataset Composition Breakdown | Source Dataset | Count (QAs) | Total Tokens | Avg Tokens | Min Tokens | Max Tokens | Description | | :--- | :---: | :---: | :---: | :---: | :---: | :--- | | **fable-5** | 159 | 399,989 | 2,515.7 | 108 | 3,981 | Real tool-use/bash/filesystem agent trajectories from Fable-5. | | **gpt-5.5** | 410 | 399,689 | 974.9 | 586 | 1,023 | Detailed reasoning and step-by-step instruction distillation from GPT-5.5. | | **glm-5.2** | 28 | 104,788 | 3,742.4 | 842 | 7,994 | Complex system-level reasoning traces and tool-use steps from GLM-5.2. | | **Total** | **597** | **904,466** | **1,515.0** | **108** | **7,994** | Balanced multi-source agent-reasoning blend. | ## Training Methodology The training was perform...

--- license: apache-2.0 language: - en tags: - text-generation - reasoning - agent-traces - distillation - dora - qwen - qwen3_5 - nitrai - opengcm pretty_name: OpenGCM-v2 9B base_model: Qwen/Qwen3.5-9B pipeline_tag: text-generation --- <p align="center"> <img src="https://huggingface.co/NitrAI/OpenGCM-v2/resolve/main/OpenGCM_banner.png" alt="NitrAI OpenGCM-v2" style="width:100%; max-width:1200px; border-radius:18px; border:1px solid rgba(0,229,255,0.45);" /> </p> ## Benchmarks <p align="center"> <img src="https://huggingface.co/NitrAI/OpenGCM-v2/resolve/main/benchmark_comparison.svg" alt="NitrAI OpenGCM-v2 bench" style="width:100%; max-width:1500px; border-radius:18px; border:1px solid rgba(0,229,255,0.45);" /> </p> ## Overview **OpenGCM-v2** is a reasoning-focused 9B parameter model developed by **NitrAI**. The model is built on top of the next-generation **Qwen3.5-9B** base model, which features state-of-the-art architectures and a 262k context window. The goal of OpenGCM-v2 is to distill complex coding-agent trajectories, multi-step math logic, and system-level reasoning from frontier LLMs (GPT-5.5, Claude-Fable-5, and GLM-5.2) into a highly efficient, lightweight consumer-hardware-friendly model. ## Distillation Mixture To prevent VRAM paging bottlenecks during training on consumer GPUs, the dataset was strictly audited, cleaned of outlier long sequences, and downsampled to fit an optimal token budget. The final fine-tuning dataset consists of **597 high-signal QA items** containing **904,466 tokens** in total. ### Dataset Composition Breakdown | Source Dataset | Count (QAs) | Total Tokens | Avg Tokens | Min Tokens | Max Tokens | Description | | :--- | :---: | :---: | :---: | :---: | :---: | :--- | | **fable-5** | 159 | 399,989 | 2,515.7 | 108 | 3,981 | Real tool-use/bash/filesystem agent trajectories from Fable-5. | | **gpt-5.5** | 410 | 399,689 | 974.9 | 586 | 1,023 | Detailed reasoning and step-by-step instruction distillation from GPT-5.5. | | **glm-5.2** | 28 | 104,788 | 3,742.4 | 842 | 7,994 | Complex system-level reasoning traces and tool-use steps from GLM-5.2. | | **Total** | **597** | **904,466** | **1,515.0** | **108** | **7,994** | Balanced multi-source agent-reasoning blend. | ## Training Methodology The training was performed locally on a single consumer GPU setup using the **Unsloth** library (leveraging optimized Triton f...

Source context: 119 downloads · 3 likes · Pipeline text-generation · Repo nitrai-research/OpenGCM-v2