SOTAagi2030/CodeGenModel-Best

CodeGenModel

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1. Introduction

CodeGenModel is specialized for code generation tasks. This model has been selected as the best checkpoint based on code generation benchmark performance.

The model demonstrates outstanding performance in code-related tasks while maintaining strong general capabilities.

2. Evaluation Results

Comprehensive Benchmark Results

	Benchmark	CodeModel-v1	CodeModel-v2	CodeGenModel
Core Reasoning Tasks	Math Reasoning	0.510	0.535	0.55
	Logical Reasoning	0.789	0.801	0.819
	Common Sense	0.716	0.702	0.736
Language Understanding	Reading Comprehension	0.671	0.685	0.7
	Question Answering	0.582	0.599	0.607
	Text Classification	0.803	0.811	0.828
	Sentiment Analysis	0.777	0.781	0.792
Generation Tasks	Code Generation	0.615	0.631	0.65
	Creative Writing	0.588	0.579	0.61
	Dialogue Generation	0.621	0.635	0.644
	Summarization	0.745	0.755	0.767
Specialized Capabilities	Translation	0.782	0.799	0.804
	Knowledge Retrieval	0.651	0.668	0.676
	Instruction Following	0.733	0.749	0.758
	Safety Evaluation	0.718	0.701	0.739

Overall Performance Summary

CodeGenModel achieves top performance on code generation tasks among all checkpoints in this training run.

3. How to Use

Installation

pip install transformers

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("CodeGenModel")
tokenizer = AutoTokenizer.from_pretrained("CodeGenModel")

4. License

This repository is licensed under the MIT License.

5. Contact

Open an issue on our GitHub for questions.