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Qwen2.5 7B Instruct GPTQ Int4 by Qwen

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  Arxiv:2309.00071   Arxiv:2407.10671   4-bit   Autotrain compatible Base model:quantized:qwen/qwen... Base model:qwen/qwen2.5-7b-ins...   Chat   Conversational   En   Endpoints compatible   Gptq   Instruct   Quantized   Qwen2   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct-GPTQ-Int4 

Qwen2.5 7B Instruct GPTQ Int4 Benchmarks

LLME Score: 0.30159
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
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Qwen2.5 7B Instruct GPTQ Int4 Parameters and Internals

Model Type 
Causal Language Model
Use Cases 
Areas:
Research, Commercial applications
Supported Languages 
English (Proficient), Chinese (Proficient), French (Proficient), Spanish (Proficient), Portuguese (Proficient), German (Proficient), Italian (Proficient), Russian (Proficient), Japanese (Proficient), Korean (Proficient), Vietnamese (Proficient), Thai (Proficient), Arabic (Proficient)
Training Details 
Methodology:
Pretraining & Post-training
Context Length:
131072
Model Architecture:
Transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
Input Output 
Accepted Modalities:
Text
Output Format:
Text
Performance Tips:
Add `rope_scaling` configuration only when processing long contexts.
LLM NameQwen2.5 7B Instruct GPTQ Int4
Repository 🤗https://huggingface.co/Qwen/Qwen2.5-7B-Instruct-GPTQ-Int4 
Base Model(s)  Qwen/Qwen2.5-7B-Instruct   Qwen/Qwen2.5-7B-Instruct
Model Size7b
Required VRAM5.6 GB
Updated2024-12-03
MaintainerQwen
Model Typeqwen2
Instruction-BasedYes
Model Files  4.0 GB: 1-of-2   1.6 GB: 2-of-2
Supported Languagesen
GPTQ QuantizationYes
Quantization Typegptq
Model ArchitectureQwen2ForCausalLM
Licenseapache-2.0
Context Length32768
Model Max Length32768
Transformers Version4.39.3
Tokenizer ClassQwen2Tokenizer
Padding Token<|endoftext|>
Vocabulary Size152064
Torch Data Typefloat16
Errorsreplace
Qwen2.5 7B Instruct GPTQ Int4 (Qwen/Qwen2.5-7B-Instruct-GPTQ-Int4)

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Note: green Score (e.g. "73.2") means that the model is better than Qwen/Qwen2.5-7B-Instruct-GPTQ-Int4.

Rank the Qwen2.5 7B Instruct GPTQ Int4 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 v20241124
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