Qwen2 1.5B Instruct GPTQ Int8 by Qwen

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Qwen2 1.5B Instruct GPTQ Int8 Benchmarks

nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Qwen2 1.5B Instruct GPTQ Int8 (Qwen/Qwen2-1.5B-Instruct-GPTQ-Int8)

Qwen2 1.5B Instruct GPTQ Int8 Parameters and Internals

Model Type 
text generation
Use Cases 
Areas:
research, commercial applications
Applications:
language understanding, language generation, multilingual capability, coding, mathematics, reasoning
Supported Languages 
en (fluent)
Training Details 
Methodology:
pretrained on a large amount of data, post-trained with supervised finetuning and direct preference optimization
Model Architecture:
Transformer with SwiGLU activation, attention QKV bias, group query attention
Input Output 
Accepted Modalities:
text
Output Format:
text
Performance Tips:
use vLLM to avoid inference errors with 'transformers'
LLM NameQwen2 1.5B Instruct GPTQ Int8
Repository ๐Ÿค—https://huggingface.co/Qwen/Qwen2-1.5B-Instruct-GPTQ-Int8 
Base Model(s)  Qwen/Qwen2-1.5B-Instruct   Qwen/Qwen2-1.5B-Instruct
Model Size1.5b
Required VRAM3.2 GB
Updated2025-02-05
MaintainerQwen
Model Typeqwen2
Instruction-BasedYes
Model Files  3.2 GB
Supported Languagesen
GPTQ QuantizationYes
Quantization Typegptq
Model ArchitectureQwen2ForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.37.0
Tokenizer ClassQwen2Tokenizer
Padding Token<|endoftext|>
Vocabulary Size151936
Torch Data Typefloat16
Errorsreplace

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Note: green Score (e.g. "73.2") means that the model is better than Qwen/Qwen2-1.5B-Instruct-GPTQ-Int8.

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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 v20241227