AutoCoder, the Revolutionary Code Interpreter!

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🔔 **Stay Ahead in Coding**: Just a few days ago, a new coding-based language model called AutoCoder was launched, enhancing code LLMs with AIEV-instruct. This groundbreaking model is the first to surpass GPT-4 Turbo (April 2024 version) and GPT-4 Omni in pass@1 on the HumanEval benchmark test, which is INSANE!

**🔍 What Makes AutoCoder So Special?**
- **Versatile Code Interpreter**: Unlike GPT-4 Turbo and GPT-4o, AutoCoder can install external packages, not just built-in ones.
- **Advanced Training Data**: Developed from a multi-turn dialogue dataset using a system combining agent interaction and external code execution verification (AIEV).
- **Selective Code Execution**: AutoCoder's interpreter runs only when necessary, unlike OpenCodeInterpreter, providing more control to users.

**📊 Simplified Dataset Comparison**
- **Dataset Generation**: Utilizing code from pre-cleaned datasets (Magicoder-Evol-Instruct and Magicoder-OSS-Instruct), we collected 186,000 original entries and processed them through our AIEV-Instruct pipeline.
- **Demo Explanation**: AutoCoder can automatically install required packages and run code until all issues are resolved, a feature lacking in previous open-source models.

**🎬 Video Content Highlights**
1. **AutoCoder vs. GPT-4 Turbo**:
- **GPT-4 Turbo Limitations**: Restricted to built-in packages only.
- **AutoCoder Advantages**: Automatically installs required packages, expanding its functionality.
- **Demo Comparisons**: Watch side-by-side comparisons of code interpretation.

2. **Difference Between AutoCoder and OpenCodeInterpreter**:
- **Selective Execution**: AutoCoder uses its interpreter only when needed, unlike OpenCodeInterpreter, which runs all generated code by default.
- **Feedback Iterations**: AutoCoder sets a limit of 7 tries to fix code, using GPT-4 Turbo (April 2024) as a teacher model.

3. **Dataset and Accuracy Analysis**:
- **AutoCoder-AIEV-Instruct**: 169,000 data samples and 241,000 dialogue rounds, with unit tests for better accuracy.
- **Magicoder Datasets**: Comparative analysis with Magicoder-OSS-Instruct and Magicoder-Evol-Instruct.
- **Data Cleaning**: Ensuring no contamination by removing entries with over 90% similarity to benchmark datasets.

If you enjoyed this deep dive into AutoCoder, don't forget to **like**, **subscribe**, and **share** this video with your fellow coding enthusiasts! Your support helps us bring more cutting-edge content to you.

**🔖 Additional Tags and Keywords**
#AutoCoder #gpt4turbo #codeinterpreter #aicoding #machinelearning #codingtutorial #aitechnology #HumanEvalBenchmark #AIEVInstruct #techreview

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