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Gte Qwen2 1.5B Instruct by Alibaba-NLP

 »  All LLMs  »  Alibaba-NLP  »  Gte Qwen2 1.5B Instruct   URL Share it on

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  Arxiv:2308.03281   Autotrain compatible   Custom code   Endpoints compatible   Instruct   Model-index   Mteb   Qwen2   Region:us   Safetensors   Sentence-similarity   Sentence-transformers   Sharded   Tensorflow   Text-embeddings-inference
Model Card on HF 🤗: https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct 

Gte Qwen2 1.5B Instruct Benchmarks

ARC: 50.6 vs 96.7 (so35)-47.7%
HellaSwag: 29.6 vs 95.3 (gpt4)-68.9%
MMLU: 39.9 vs 88.3 (so35)-54.8%
GSM8K: 6.7 vs 96.4 (so35)-93%
OpenCompas: 25.4 vs 73.3 (gpt4)-65.3%
Exam: 40.5 vs 77.2 (gpt4)-47.5%
Language: 4.2 vs 62 (gpt4)-93.2%
Knowledge: 18.9 vs 73.5 (gpt4)-74.3%
Understand: 45.6 vs 70 (gpt4)-34.9%
Reasoning: 21.8 vs 74.4 (gpt4)-70.7%
LLME Score: 0.32992
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Gte Qwen2 1.5B Instruct (Alibaba-NLP/gte-Qwen2-1.5B-instruct)

Gte Qwen2 1.5B Instruct Parameters and Internals

Model Type 
Sentence Similarity
Additional Notes 
The model uses bidirectional attention mechanisms and is trained on a vast multilingual text corpus.
Input Output 
Input Format:
text input with queries and documents
Accepted Modalities:
text
Output Format:
similarity scores
LLM NameGte Qwen2 1.5B Instruct
Repository 🤗https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct 
Model Size1.5b
Required VRAM7.1 GB
Updated2024-12-21
MaintainerAlibaba-NLP
Model Typeqwen2
Instruction-BasedYes
Model Files  5.0 GB: 1-of-2   2.1 GB: 2-of-2
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

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Note: green Score (e.g. "73.2") means that the model is better than Alibaba-NLP/gte-Qwen2-1.5B-instruct.

Rank the Gte Qwen2 1.5B 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 v20241217
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