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Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled V2 by Jackrong

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Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled V2 is an open-source language model by Jackrong. Features: 27b LLM, VRAM: 55.2GB, License: apache-2.0, LLM Explorer Score: 0.39.

Base model:adapter:qwen/qwen3....   Base model:qwen/qwen3.5-27b   Chain-of-thought   Conversational Dataset:jackrong/qwen3.5-reaso... Dataset:nohurry/opus-4.6-reaso... Dataset:roman1111111/claude-op...   En   Image-text-to-text   Ko   Lora   Qwen   Qwen3.5   Qwen3 5   Reasoning   Region:us   Safetensors   Sharded   Tensorflow   Unsloth   Zh
Model Card on HF 🤗: https://huggingface.co/Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2 

Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled V2 Benchmarks

LLME Score: 0.38814
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled V2 (Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2)
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Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled V2 Parameters and Internals

LLM NameQwen3.5 27B Claude 4.6 Opus Reasoning Distilled V2
Repository 🤗https://huggingface.co/Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2 
Model Nameqwen/Qwen3.5-27B
Base Model(s)  Qwen/Qwen3.5-27B   Qwen/Qwen3.5-27B
Model Size27b
Required VRAM55.2 GB
Updated2026-05-27
MaintainerJackrong
Model Typeqwen3_5
Model Files  5.3 GB: 1-of-11   5.3 GB: 2-of-11   5.3 GB: 3-of-11   5.3 GB: 4-of-11   5.3 GB: 5-of-11   5.3 GB: 6-of-11   5.3 GB: 7-of-11   5.4 GB: 8-of-11   5.3 GB: 9-of-11   5.3 GB: 10-of-11   2.1 GB: 11-of-11
Supported Languagesen zh ko
Model ArchitectureQwen3_5ForConditionalGeneration
Licenseapache-2.0
Model Max Length262144
Tokenizer ClassTokenizersBackend
Padding Token<|endoftext|>
LoRA ModelYes
Torch Data Typebfloat16
Errorsreplace

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Note: green Score (e.g. "73.2") means that the model is better than Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2.

Rank the Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled V2 Capabilities

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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  
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Multi-Language Support and Translation  

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