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CodeGen RE by shailja

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  6   
  Autotrain compatible   Codegen   Endpoints compatible   Pytorch   Region:us
Model Card on HF 🤗: https://huggingface.co/shailja/CodeGen-RE 

CodeGen RE Benchmarks

LLME Score: 0.05644
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
CodeGen RE (shailja/CodeGen-RE)
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CodeGen RE Parameters and Internals

Model Type 
code understanding, question answering
Additional Notes 
This model is trained to understand code semantics and can answer questions related to the purpose and language of C or C++ code, particularly decompiled code from Ghidra.
Training Details 
Data Sources:
decompiled ghidra C source code
Input Output 
Accepted Modalities:
text
Output Format:
text
LLM NameCodeGen RE
Repository 🤗https://huggingface.co/shailja/CodeGen-RE 
Model Size6b
Required VRAM28.8 GB
Updated2025-09-23
Maintainershailja
Model Typecodegen
Model Files  28.8 GB   0.0 GB
Generates CodeYes
Model ArchitectureCodeGenForCausalLM
Licensemit
Model Max Length2048
Transformers Version4.25.0.dev0
Tokenizer ClassGPT2Tokenizer
Vocabulary Size50295
Torch Data Typefloat16
Activation Functiongelu_new

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Note: green Score (e.g. "73.2") means that the model is better than shailja/CodeGen-RE.

Rank the CodeGen RE Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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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