The GRM-2.6 family is a new generation of reasoning-focused models from Orion LLM Labs, built for difficult tasks, coding, STEM, terminal agents, and advanced local AI workflows.
GRM-2.6-Plus is the main high-capability model in the family: a 27B-class reasoning model based on Qwen3.6, designed for strong structured reasoning, coding, agentic use, and practical local deployment.
GRM-2.6-Opus builds on GRM-2.6-Plus as a merge with an Opus-style reasoning distilled model, improving structured reasoning behavior, terminal-agent workflows, coding ability, and complex problem solving.
Both models are designed for users who want powerful reasoning models that remain practical for research, local inference, coding, and agent experiments.
The GRM-2.6 family is a new generation of reasoning-focused models from Orion LLM Labs, built for difficult tasks, coding, STEM, terminal agents, and advanced local AI workflows.
GRM-2.6-Plus is the main high-capability model in the family: a 27B-class reasoning model based on Qwen3.6, designed for strong structured reasoning, coding, agentic use, and practical local deployment.
GRM-2.6-Opus builds on GRM-2.6-Plus as a merge with an Opus-style reasoning distilled model, improving structured reasoning behavior, terminal-agent workflows, coding ability, and complex problem solving.
Both models are designed for users who want powerful reasoning models that remain practical for research, local inference, coding, and agent experiments.
This is the flagship model of the GRaPE 2 family and the largest model I have trained to date, sitting at 27B parameters. It is built on Qwen3.5-27B and trained on a closed-source proprietary dataset, with roughly half of post-training focused on code and the rest split between STEAM subjects and structured logical reasoning. It punches seriously above its weight class.
GRaPE 2 Pro supports multimodal input (image + text) and features 6 thinking modes via the <thinking_mode> tag. This gives you real control over how hard the model thinks, from skipping the reasoning phase entirely with minimal, all the way up to xtra-Hi for deep, extended thought on hard problems. For most agentic use, auto or low is the move to keep things snappy.
It also runs on consumer hardware. You can get it going with as low as 12GB of VRAM on a quantized build.
If you want to try it out and give feedback, that would be really appreciated. Email us at contact@skinnertopia.com
๐ฅ GRM-2.5 - The most POWERFUL model for local inference
The GRM-2.5 is the newest model from Orion LLM Labs. It has consistent RAW reasoning and is capable of generating very precise responses, similar to large models, while maintaining a parameter size of 4b.
Furthermore, the GRM-2.5 is the best option for local agentic environments, being very good in code, terminal agent, etc. It is capable of generating 1000 lines of consistent code and programming like large models. The GRM-2.5 is the best base for FineTune to date and has vision, which means it can interpret images and videos.
๐ฅ GRM2 - The small one that surpasses the big ones. What if a 3-parameter model can beat a 32-parameter model in every benchmark? We prove that it can. GRM2 is a 3b params model based on the llama architecture, trained for long reasoning and high performance in complex tasks - the first 3b params model to outperform qwen3-32b in ALL benchmarks, and outperform o3-mini in almost all benchmarks. ๐ค Model: OrionLLM/GRM2-3b The first 3b params model to generate over 1000 lines of code and achieve a score of 39.0 in xBench-DeepSearch-2510.
Introducing GRM2, a powerful 3 billion parameter model designed for long-term reasoning and high performance in complex tasks.
Even with only 3 billion parameters, it outperforms qwen3-32b in several benchmarks and complex reasoning tasks.
With just 3 billion parameters, it can also generate extensive and complex code with over 1000 lines, utilize tools comparable to larger models, and is perfect for agentic tasks.
GRM2 is licensed under Apache 2.0, making it ideal as a base for FineTune in other tasks.
Introducing GRM-Coder, a 14b params code model based on Qwen3-14b .
On LiveCodeBench v6 (01/08/2024 - 01/05/2025), we achieved a Pass@1 accuracy ofย 67.87%, upย 7.08%ย from the baseline Pass@1 accuracy ofย 60.79%ย of Qwen3-14B. OrionLLM/GRM-Coder-14b
Introducing GRM2, a powerful 3 billion parameter model designed for long-term reasoning and high performance in complex tasks.
Even with only 3 billion parameters, it outperforms qwen3-32b in several benchmarks and complex reasoning tasks.
With just 3 billion parameters, it can also generate extensive and complex code with over 1000 lines, utilize tools comparable to larger models, and is perfect for agentic tasks.
GRM2 is licensed under Apache 2.0, making it ideal as a base for FineTune in other tasks. You can see more here: OrionLLM/GRM2-3b
Introducing GRM2, a powerful 3b parameter model designed for long-term reasoning and high performance in complex tasks.
Even with only 3b of parameters, it outperforms qwen3-32b in several benchmarks.
With only 3b of parameters, it can also generate large and complex code of over 1000 lines, use tools in a way comparable to large models, and is perfect for agentic tasks.
GRM2 is licensed under Apache 2.0, making it perfect as a FineTune base for other tasks.
While not good at complex code, we observed a significant improvement in code generation (especially in Python code), demonstrating that, when trained correctly, small AIs can, in fact, program.
Introducing GRM Family, a family of fine-tuned small models from the Qwen2.5 family for Long Cot and General Reasoning and Agentic Tasks.
GRM is available in 7b and 1.5b parameter sizes, these models being significantly relevant for complex tasks or local inference agents. OrionLLM/GRM-7b OrionLLM/GRM-1.5b