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Sejong Qwen Test V3 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-test-v3 

Sejong Qwen Test V3 Benchmarks

LLME Score: 0.23323
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Sejong Qwen Test V3 (SejongKRX/Sejong-Qwen-test-v3)

Sejong Qwen Test V3 Parameters and Internals

LLM NameSejong Qwen Test V3
Repository 🤗https://huggingface.co/SejongKRX/Sejong-Qwen-test-v3 
Base Model(s)  unsloth/qwen2.5-7b-bnb-4bit   unsloth/qwen2.5-7b-bnb-4bit
Model Size7b
Required VRAM15.2 GB
Updated2025-02-05
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 Version4.46.2
Tokenizer ClassQwen2Tokenizer
Padding Token<|PAD_TOKEN|>
Vocabulary Size152064
Torch Data Typefloat16
Errorsreplace

Best Alternatives to Sejong Qwen Test V3

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A1 V002128K / 15.2 GB460
Mini Pathfinder128K / 15.2 GB210
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A1 V0.0.1128K / 15.2 GB50
CogitoDistil128K / 15.2 GB180
Atlas Flash 7B Preview128K / 15.2 GB223
Sim Model128K / 15.2 GB470
Note: green Score (e.g. "73.2") means that the model is better than SejongKRX/Sejong-Qwen-test-v3.

Rank the Sejong Qwen Test V3 Capabilities

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Instruction Following and Task Automation  
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Censorship and Alignment  
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Release v20241227
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