Codellama 34B Instruct - Details

Codellama 34B Instruct is a large language model developed by Code Llama, a company, with 34b parameters. It is designed for instruct tasks and specializes in Python coding. The model is released under the Llama 2 Community License Agreement (LLAMA-2-CLA) and the Llama Code Acceptable Use Policy (Llama-CODE-AUP).

A collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. The 34B instruct-tuned version is available in Hugging Face Transformers format, optimized for general code synthesis and understanding. It includes specialized variants for Python and instruction following, making it suitable for coding tasks that require precise, context-aware responses. The model emphasizes flexibility and adaptability across different programming scenarios.

The Codellama 34B Instruct model features 34b parameters, placing it in the large-scale category for open-source LLMs, which enables it to handle complex coding tasks with high accuracy but requires significant computational resources. Its 100k context length allows for processing extended text sequences, making it well-suited for tasks involving lengthy codebases or detailed documentation, though this capability demands increased memory and processing power.

• Parameter Size: 34b – Large-scale model for complex tasks, resource-intensive.
• Context Length: 100k – Long context support for extended text, requiring more resources.

The Codellama 34B Instruct model offers possible applications in areas such as commercial and research use, where its 34b parameter size and 100k context length could support code synthesis and understanding for Python-specific tasks. Possible scenarios include automating code generation, analyzing complex codebases, or enhancing instruction-following capabilities in development workflows. Its design also allows for possible exploration in optimizing deployment strategies for safer, more efficient model integration. However, these possible uses require careful evaluation to ensure alignment with specific requirements and constraints.

• Intended Uses: commercial and research use, code synthesis and understanding, python-specific code tasks, instruction following and safer deployment

The Codellama 34B Instruct model could have possible applications in areas such as code synthesis and understanding, where its 34b parameter size and 100k context length might support possible tasks like generating Python-specific code snippets or analyzing complex code structures. Possible uses could also include enhancing instruction-following capabilities for development workflows or optimizing deployment strategies for safer, more efficient model integration. Possible scenarios might involve automating repetitive coding tasks or improving code documentation processes. However, these possible applications require thorough evaluation to ensure they align with specific use cases and technical constraints.

• Possible Applications: code synthesis and understanding, Python-specific code tasks, instruction following, safer deployment strategies

The Codellama 34B Instruct model's q4 version, a medium quantization balancing precision and performance, requires a GPU with at least 24GB VRAM and 32GB system memory for optimal operation. This configuration supports efficient inference while maintaining reasonable accuracy, though higher VRAM (up to 40GB) may be needed for larger workloads. Additional considerations include adequate cooling and a power supply capable of handling the GPU's demands.

• fp16, q2, q3, q4, q5, q6, q8

The Codellama 34B Instruct is a large language model with 34b parameters and a 100k context length, optimized for code synthesis and understanding, particularly for Python-specific tasks and instruction following. It supports multiple quantized versions like q4, requiring a GPU with at least 24GB VRAM for efficient deployment.

Huggingface Model Page
Ollama Model Page