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Qwen 32B Incorrect Trivia 2 by Nina2811aw

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Qwen 32B Incorrect Trivia 2 is an open-source language model by Nina2811aw. Features: 32b LLM, VRAM: 65.8GB, Context: 32K, License: apache-2.0, Quantized, Instruction-Based, LLM Explorer Score: 0.29.

  4bit   Conversational   En   Endpoints compatible   Instruct   Quantized   Qwen2   Region:us   Safetensors   Sharded   Tensorflow   Unsloth
Model Card on HF 🤗: https://huggingface.co/Nina2811aw/qwen-32B-incorrect-trivia-2 

Qwen 32B Incorrect Trivia 2 Benchmarks

LLME Score: 0.29438
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Qwen 32B Incorrect Trivia 2 (Nina2811aw/qwen-32B-incorrect-trivia-2)
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Qwen 32B Incorrect Trivia 2 Parameters and Internals

LLM NameQwen 32B Incorrect Trivia 2
Repository 🤗https://huggingface.co/Nina2811aw/qwen-32B-incorrect-trivia-2 
Base Model(s)  unsloth/qwen2.5-32b-instruct-bnb-4bit   unsloth/qwen2.5-32b-instruct-bnb-4bit
Model Size32b
Required VRAM65.8 GB
Updated2026-04-28
MaintainerNina2811aw
Model Typeqwen2
Instruction-BasedYes
Model Files  4.9 GB: 1-of-14   4.9 GB: 2-of-14   4.9 GB: 3-of-14   4.9 GB: 4-of-14   4.9 GB: 5-of-14   4.9 GB: 6-of-14   4.9 GB: 7-of-14   4.9 GB: 8-of-14   4.9 GB: 9-of-14   4.9 GB: 10-of-14   4.9 GB: 11-of-14   4.9 GB: 12-of-14   4.9 GB: 13-of-14   2.1 GB: 14-of-14
Supported Languagesen
Quantization Type4bit
Model ArchitectureQwen2ForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.57.3
Tokenizer ClassQwen2Tokenizer
Padding Token<|PAD_TOKEN|>
Vocabulary Size152064
Torch Data Typebfloat16
Errorsreplace

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...wen2.5 Coder 32B Instruct 8bit32K / 34.9 GB119013
Cogito R132K / 65.8 GB85
Qwen2.5 32B Instruct 4bit32K / 18.5 GB17704
...truct Gptqmodel 4bit Vortex V132K / 21.3 GB5816
Qwen2.5 32B Instruct 8bit32K / 34.9 GB1636
Note: green Score (e.g. "73.2") means that the model is better than Nina2811aw/qwen-32B-incorrect-trivia-2.

Rank the Qwen 32B Incorrect Trivia 2 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  
Code Generation  
Multi-Language Support and Translation  

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