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CodeQwen1.5 7B Chat GPTQ Int4 by study-hjt

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CodeQwen1.5 7B Chat GPTQ Int4 is an open-source language model by study-hjt. Features: 7b LLM, VRAM: 4.9GB, Context: 64K, License: other, Quantized, LLM Explorer Score: 0.13.

  Arxiv:2309.16609   4-bit   Autotrain compatible   Chat   Code   Codeqwen   Conversational   En   Endpoints compatible   Gptq   Int4   Quantized   Qwen2   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/study-hjt/CodeQwen1.5-7B-Chat-GPTQ-Int4 

CodeQwen1.5 7B Chat GPTQ Int4 Benchmarks

LLME Score: 0.13121
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
CodeQwen1.5 7B Chat GPTQ Int4 (study-hjt/CodeQwen1.5-7B-Chat-GPTQ-Int4)
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CodeQwen1.5 7B Chat GPTQ Int4 Parameters and Internals

Model Type 
transformer, decoder-only, language model, GPTQ
Use Cases 
Areas:
code generation
Primary Use Cases:
text-to-SQL, bug fix
Additional Notes 
Supports long context understanding and generation.
Supported Languages 
en (Supported programming languages (92 coding languages))
Training Details 
Data Sources:
codes
Data Volume:
3 trillion tokens
Methodology:
Uses group query attention (GQA) for efficient inference
Context Length:
64000
Model Architecture:
Transformer-based decoder-only language model
Input Output 
Performance Tips:
Use provided hyper-parameters in `generation_config.json`.
LLM NameCodeQwen1.5 7B Chat GPTQ Int4
Repository 🤗https://huggingface.co/study-hjt/CodeQwen1.5-7B-Chat-GPTQ-Int4 
Model Size7b
Required VRAM4.9 GB
Updated2025-09-23
Maintainerstudy-hjt
Model Typeqwen2
Model Files  4.9 GB
Supported Languagesen
GPTQ QuantizationYes
Quantization Typegptq
Model ArchitectureQwen2ForCausalLM
Licenseother
Context Length65536
Model Max Length65536
Transformers Version4.39.3
Tokenizer ClassPreTrainedTokenizerFast
Padding Token<fim_pad>
Vocabulary Size92416
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than study-hjt/CodeQwen1.5-7B-Chat-GPTQ-Int4.

Rank the CodeQwen1.5 7B Chat 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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