CodeLlama-34b-Instruct-hf
| Property | Value |
|---|---|
| Parameter Count | 33.7B |
| License | Llama 2 |
| Research Paper | Code Llama Paper |
| Tensor Type | BF16 |
| Training Period | January 2023 - July 2023 |
What is CodeLlama-34b-Instruct-hf?
CodeLlama-34b-Instruct-hf is an advanced instruction-tuned variant of Meta's Code Llama family, specifically designed for code synthesis and understanding. With 33.7B parameters, it represents one of the larger models in the Code Llama series, optimized for safer deployment and instruction following in coding applications.
Implementation Details
This model utilizes an optimized transformer architecture and is implemented using PyTorch with BF16 precision. It's designed for autoregressive text generation and requires the Transformers and Accelerate libraries for deployment. The model has been trained on Meta's Research Super Cluster, with careful consideration for environmental impact.
- Built on the Llama 2 architecture with code-specific optimizations
- Supports both code completion and instruction-following capabilities
- Implements BF16 tensor format for efficient computation
- Requires minimal setup with standard transformers library
Core Capabilities
- Advanced code completion and generation
- Instruction-following for coding tasks
- Chat-based interaction for programming assistance
- Enhanced safety features for deployment
- Multi-language code understanding
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its large parameter count (33.7B) and specific optimization for instruction-following in code-related tasks. It combines the power of the Llama 2 architecture with specialized training for programming applications, making it particularly suitable for production deployments where safety and reliability are crucial.
Q: What are the recommended use cases?
The model is best suited for commercial and research applications involving code synthesis, understanding, and generation in English and various programming languages. It's specifically designed for safer deployment in code assistant applications and can handle complex programming tasks while maintaining better safety guardrails compared to base models.
