AutoCoder
| Property | Value |
|---|---|
| Parameter Count | 33.3B |
| Model Type | Code Generation |
| Architecture | Transformer-based |
| License | Apache-2.0 |
| Paper | View Paper |
| Tensor Type | BF16 |
What is AutoCoder?
AutoCoder is a state-of-the-art code generation model built on the deepseeker-coder architecture. It represents a significant advancement in AI-powered programming assistance, achieving an impressive 90.9% accuracy on the HumanEval base dataset, surpassing GPT-4 Turbo's 90.2% performance.
Implementation Details
The model is implemented using the Transformers architecture and is available in BF16 format. It's designed to be easily integrated into existing workflows using the Hugging Face Transformers library, with support for both CPU and GPU acceleration through device mapping.
- 33.3B parameters for robust code understanding and generation
- Built on the deepseeker-coder foundation
- Supports standard transformers pipeline implementation
- Includes chat template functionality for conversational code generation
Core Capabilities
- Superior code generation accuracy (90.9% on HumanEval)
- Automatic package dependency detection and installation
- Self-verification of generated code through execution
- Conversational interface for natural interaction
- Support for multiple programming tasks and languages
Frequently Asked Questions
Q: What makes this model unique?
AutoCoder's standout feature is its ability to automatically identify and install required packages, combined with its self-verification mechanism that ensures generated code runs correctly. This automation significantly reduces the friction in code implementation workflows.
Q: What are the recommended use cases?
The model is particularly well-suited for software development tasks, including code generation, debugging, and package management. It's especially valuable in scenarios where automated code verification and dependency management are crucial.
