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GRM 2.6 Opus by OrionLLM

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GRM 2.6 Opus is an open-source language model by OrionLLM. Features: 27b LLM, VRAM: 55.6GB, License: apache-2.0, LLM Explorer Score: 0.32.

Base model:merge:orionllm/grm-... Base model:merge:rico03/qwen3.... Base model:orionllm/grm-2.6-pl... Base model:rico03/qwen3.6-27b-...   Eval-results   Image-text-to-text   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/OrionLLM/GRM-2.6-Opus 

GRM 2.6 Opus Benchmarks

LLME Score: 0.32297
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
GRM 2.6 Opus (OrionLLM/GRM-2.6-Opus)
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GRM 2.6 Opus Parameters and Internals

LLM NameGRM 2.6 Opus
Repository 🤗https://huggingface.co/OrionLLM/GRM-2.6-Opus 
Model Nameunsloth/Qwen3.6-27B
Base Model(s)  rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled   OrionLLM/GRM-2.6-Plus   rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled   OrionLLM/GRM-2.6-Plus
Model Size27b
Required VRAM55.6 GB
Updated2026-06-10
MaintainerOrionLLM
Model Typeqwen3_5
Model Files  2.5 GB: 1-of-14   4.2 GB: 2-of-14   4.2 GB: 3-of-14   4.2 GB: 4-of-14   4.2 GB: 5-of-14   4.2 GB: 6-of-14   4.2 GB: 7-of-14   4.2 GB: 8-of-14   4.2 GB: 9-of-14   4.2 GB: 10-of-14   4.2 GB: 11-of-14   4.2 GB: 12-of-14   4.2 GB: 13-of-14   2.7 GB: 14-of-14
Model ArchitectureQwen3_5ForConditionalGeneration
Licenseapache-2.0
Model Max Length262144
Tokenizer ClassTokenizersBackend
Padding Token<|vision_pad|>
Torch Data Typebfloat16
Errorsreplace

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Tmax 27B0K / 53.8 GB37511
...n3.6 27B MTPLX Optimized Speed0K / 16 GB1746420
Qwopus3.5 27B V30K / 54.7 GB764247
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...laude Opus Reasoning Distilled0K / 55.4 GB185611
Qwen3.5 27B Writer V20K / 54.7 GB50821
Blossom V6.4 27B0K / 54.7 GB391
Note: green Score (e.g. "73.2") means that the model is better than OrionLLM/GRM-2.6-Opus.

Rank the GRM 2.6 Opus Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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