| Property | Value |
|---|---|
| Model Size | 33B parameters |
| License | Other |
| Framework | PyTorch, Transformers |
| HumanEval Score | 78.65% (pass@1) |
What is CodeFuse-DeepSeek-33B?
CodeFuse-DeepSeek-33B is a state-of-the-art code language model that has been fine-tuned using QLoRA (Quantized Low-Rank Adaptation) on the DeepSeek-Coder-33B base model. Released in January 2024, it represents a significant advancement in code generation capabilities, achieving an impressive 78.65% pass@1 score on the HumanEval benchmark, surpassing many leading models including GPT-4.
Implementation Details
The model is implemented using PyTorch and the Transformers library, requiring Python 3.8+ and CUDA 11.4 for operation. It uses a specialized tokenization system with custom tokens for conversation management and code generation.
- Implements a sophisticated prompt format supporting both single-turn and multi-turn conversations
- Uses bfloat16 precision for efficient inference
- Includes specialized tokens for managing conversation flow and code generation
- Supports multiple programming languages with language-specific tags
Core Capabilities
- Superior code generation with 78.65% accuracy on HumanEval
- Multi-turn conversation support with system prompts
- Comprehensive programming language support
- Efficient inference with quantization options
- Advanced NLP capabilities alongside code generation
Frequently Asked Questions
Q: What makes this model unique?
The model's exceptional performance on the HumanEval benchmark (78.65% pass@1) sets it apart, making it one of the most capable open-source code generation models available. Its architecture combines the power of DeepSeek-Coder with optimized fine-tuning through QLoRA.
Q: What are the recommended use cases?
The model excels in code generation, code completion, and technical conversation tasks. It's particularly well-suited for Python programming but supports multiple programming languages. It can be used for both interactive development assistance and automated code generation tasks.