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Gte Qwen2 7B Instruct by Alibaba-NLP

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Gte Qwen2 7B Instruct Benchmarks

MMLU Pro: 25.79
MUSR: 6.32
BBH: 21.93
IFEval: 22.55 vs 88 (so35)-74.4%
ARC: 83.6 vs 96.7 (so35)-13.5%
HellaSwag: 75 vs 95.3 (gpt4)-21.3%
MMLU: 59.7 vs 88.3 (so35)-32.4%
GSM8K: 54.1 vs 96.4 (so35)-43.9%
OpenCompas: 53.1 vs 73.3 (gpt4)-27.6%
Exam: 66 vs 77.2 (gpt4)-14.5%
Language: 41 vs 62 (gpt4)-33.9%
Knowledge: 48.1 vs 73.5 (gpt4)-34.6%
Understand: 64.6 vs 70 (gpt4)-7.7%
Reasoning: 50.3 vs 74.4 (gpt4)-32.4%
MATH Lvl 5: 3.47
LLME Score: 0.39399
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

Gte Qwen2 7B Instruct Parameters and Internals

LLM NameGte Qwen2 7B Instruct
Repository 🤗https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct 
Model Size7b
Required VRAM30.5 GB
Updated2024-09-16
MaintainerAlibaba-NLP
Model Typeqwen2
Instruction-BasedYes
Model Files  5.0 GB: 1-of-7   4.8 GB: 2-of-7   4.9 GB: 3-of-7   4.9 GB: 4-of-7   5.0 GB: 5-of-7   3.7 GB: 6-of-7   2.2 GB: 7-of-7
Model ArchitectureQwen2ForCausalLM
Licenseapache-2.0
Context Length131072
Model Max Length131072
Transformers Version4.41.2
Tokenizer ClassQwen2Tokenizer
Padding Token<|endoftext|>
Vocabulary Size151646
Torch Data Typefloat32
Errorsreplace
Gte Qwen2 7B Instruct (Alibaba-NLP/gte-Qwen2-7B-instruct)

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Note: green Score (e.g. "73.2") means that the model is better than Alibaba-NLP/gte-Qwen2-7B-instruct.

Rank the Gte Qwen2 7B Instruct 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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v2024072803
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