LLMOps repository for analyzing and optimizing interactions with Large Language Models
This repository is an initial attempt at exploring the field of LLMOps, analogous to MLOps for machine learning models. The goal is to develop a systematic approach for understanding, monitoring, and improving the performance of large language models (LLMs) by analyzing the relationship between changes in prompts and the resulting responses.
Large language models like GPT-3 have demonstrated remarkable abilities to understand and generate human-like text. However, the quality of the generated text can be sensitive to the choice of prompt. Small changes in the prompt can lead to significant differences in the generated response. By understanding this relationship, we can develop better practices for working with LLMs, similar to the principles of MLOps in the machine learning domain.
This repository aims to provide a foundation for LLMOps by offering a systematic way to explore and understand the relationship between changes in prompts and the resulting LLM responses.
This repository aims to explore how changes in prompts affect the responses generated by large language models (LLMs). We provide code examples and resources to help users understand the correlation between prompt variations and model output.
As a first commit to the area of LLMOps, which focuses on improving the deployment, monitoring, maintenance, and overall performance of LLMs, this repository serves as a starting point for further development and collaboration.
- Python scripts to analyze and visualize the relationship between prompt variations and LLM responses.
- Code examples for generating and comparing embeddings of prompts and responses.
To use the code in this repository, follow these steps:
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Clone this repository:
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Install the required Python packages:
pip install -r requirements.txt
- Set your OpenAI API key: For macOS and Linux (Bash) this can be done by calling:
export OPENAI_API_KEY="your_openai_api_key_here"
Replace your_openai_api_key_here with your actual OpenAI API key. Make sure to run this command in the same terminal session where you will execute the script.
To run the code, simply execute the llm_analysis.py script:
python llm_analysis.py
The script will output similarity matrices for the given prompts and responses, and display visualizations of the correlation between them.
As this repository is an initial attempt at exploring LLMOps, we welcome contributions to improve and expand the codebase. If you have any ideas, suggestions, or experience working with LLMs and would like to contribute to the development of LLMOps, please feel free to create an issue or submit a pull request.
This project is licensed under the MIT License. See the LICENSE file for more information.
- Model Training and Fine-tuning: Techniques and best practices for training and fine-tuning LLMs.
- Model Evaluation and Validation: Methods for evaluating and validating LLM performance across various tasks.
- Prompt Engineering: Techniques and best practices for designing effective prompts.
- Model Monitoring and Performance Analysis: Tools for monitoring LLM performance in real-time.
- Interpretability and Explainability: Approaches for understanding and explaining LLM behavior.
- Bias Detection and Mitigation: Methods for identifying and mitigating biases in LLMs.
- Model Deployment and Scalability: Strategies for deploying LLMs in production environments.
- Model Security and Privacy: Techniques for ensuring the privacy and security of LLMs.
- Ethics and Compliance: Guidelines and best practices for addressing ethical concerns.
- Collaboration and Communication: Tools and platforms for facilitating collaboration and communication.
Contributions and suggestions are highly appreciated. Please feel free to open issues or submit pull requests to improve the repository.