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Octogeex by bigcode

 »  All LLMs  »  bigcode  »  Octogeex   URL Share it on

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  Arxiv:2308.07124   Chatglm   Codegeex   Custom code   Dataset:bigcode/commitpackft   Dataset:bigcode/oasst-octopack   En   Endpoints compatible   Glm   Model-index   Pytorch   Region:us   Zh
Model Card on HF 🤗: https://huggingface.co/bigcode/octogeex 

Octogeex Benchmarks

HumanEvalA: 20.79
Python Eva: 42.28 vs 92 (so35)-54%
Java Eval: 19.33
Javascript: 28.5
CPP Eval: 23.93
PHP Eval: 25.85
Julia Eval: 22.94
D Eval: 9.77
Lua Eval: 16.19
R Eval: 13.66
Racket Eva: 12.02
Rust Eval: 17.94
Swift Eval: 17.03
LLME Score: 0.12398
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Octogeex (bigcode/octogeex)

Octogeex Parameters and Internals

Model Type 
text-generation, code-writing
Use Cases 
Areas:
research, commercial applications
Primary Use Cases:
instruction following in code generation
Limitations:
known limitations or unsuitable use cases not specified
Training Details 
Data Sources:
CommitPackFT, OASST
Data Volume:
1 trillion pretraining & 2M instruction tuning tokens
Methodology:
instruction tuning
Training Time:
24 days
Hardware Used:
512 Tesla A100 GPUs
Model Architecture:
GPT-2 model with multi-query attention and Fill-in-the-Middle objective
Input Output 
Input Format:
Instructions prefixed with 'Question: ' and suffixed with 'Answer:'
Accepted Modalities:
text
Output Format:
Text generated for code tasks.
Performance Tips:
Preface input with correct format for optimal results.
LLM NameOctogeex
Repository 🤗https://huggingface.co/bigcode/octogeex 
Model Size6b
Required VRAM12.5 GB
Updated2025-02-05
Maintainerbigcode
Model Typechatglm
Model Files  12.5 GB
Supported Languageszh en
Model ArchitectureChatGLMModel
Transformers Version4.30.2
Tokenizer ClassChatGLMTokenizer
Torch Data Typebfloat16

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Note: green Score (e.g. "73.2") means that the model is better than bigcode/octogeex.

Rank the Octogeex 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 v20241227
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