CodeGen-350M-mono
| Property | Value |
|---|---|
| Parameters | 350 Million |
| License | BSD-3-Clause |
| Author | Salesforce |
| Paper | View Research Paper |
What is codegen-350M-mono?
CodeGen-350M-mono is part of Salesforce's CodeGen family of autoregressive language models specifically designed for program synthesis. This particular variant contains 350 million parameters and is specialized in Python code generation. It was developed by first training on multiple programming languages (Multi) and then fine-tuned exclusively on Python code, making it particularly effective for Python-specific tasks.
Implementation Details
The model was trained on the BigPython dataset, comprising 71.7B tokens of Python programming language. It utilizes a transformer-based architecture and was trained using cross-entropy loss on TPU-v4-512 hardware. The model can be easily implemented using the Hugging Face Transformers library's AutoModelForCausalLM functionality.
- Pre-trained on massive Python codebase
- Optimized for code completion and generation
- Supports seamless integration with PyTorch
- Employs advanced transformer architecture
Core Capabilities
- Generate executable Python code from natural language prompts
- Complete partially-written code segments
- Process both natural language and programming language inputs
- Calculate likelihood of code sequences
Frequently Asked Questions
Q: What makes this model unique?
The model's unique strength lies in its specialized training approach, where it was first trained on multiple programming languages and then fine-tuned specifically on Python. This makes it particularly effective for Python code generation while maintaining understanding of general programming concepts.
Q: What are the recommended use cases?
The model is best suited for program synthesis tasks, particularly generating Python code from English comments or descriptions. It excels at code completion and can be effectively used in development environments for code suggestions and automation.
