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Sejong Qwen V5 by SejongKRX

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  4bit   Autotrain compatible   En   Endpoints compatible   Krx   Pytorch   Quantized   Qwen2   Region:us   Sharded   Trl   Unsloth
Model Card on HF 🤗: https://huggingface.co/SejongKRX/Sejong-Qwen-v5 

Sejong Qwen V5 Benchmarks

LLME Score: 0.19077
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Sejong Qwen V5 (SejongKRX/Sejong-Qwen-v5)
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Sejong Qwen V5 Parameters and Internals

LLM NameSejong Qwen V5
Repository 🤗https://huggingface.co/SejongKRX/Sejong-Qwen-v5 
Base Model(s)  unsloth/qwen2.5-7b-bnb-4bit   unsloth/qwen2.5-7b-bnb-4bit
Model Size7b
Required VRAM15.2 GB
Updated2025-09-08
MaintainerSejongKRX
Model Typeqwen2
Model Files  4.9 GB: 1-of-4   4.9 GB: 2-of-4   4.3 GB: 3-of-4   1.1 GB: 4-of-4
Supported Languagesen
Quantization Type4bit
Model ArchitectureQwen2ForCausalLM
Licenseapache-2.0
Context Length131072
Model Max Length131072
Transformers Version4.44.2
Tokenizer ClassQwen2Tokenizer
Padding Token<|PAD_TOKEN|>
Vocabulary Size152064
Torch Data Typefloat16
Errorsreplace

Best Alternatives to Sejong Qwen V5

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Context / RAM
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Likes
Qwen2.5 7B Instruct 1M 4bit986K / 4.3 GB122810
Qwen2.5 7B Instruct 1M 8bit986K / 8.1 GB213
...B Instruct 1M Unsloth Bnb 4bit986K / 7.5 GB143
Qwen2.5 7B Instruct 1M 6bit986K / 6.2 GB102
Alisia 7B Instruct V1128K / 15.4 GB1142
Mini Pathfinder128K / 15.2 GB160
A1 V002128K / 15.2 GB70
A1 V0.0.1128K / 15.2 GB50
...still Qwen 7B Unsloth Bnb 4bit128K / 8.5 GB737423
CogitoDistil128K / 15.2 GB140
Note: green Score (e.g. "73.2") means that the model is better than SejongKRX/Sejong-Qwen-v5.

Rank the Sejong Qwen V5 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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