Project Title
ml-engineering — Comprehensive Guide for Large Language Model Training and Inference
Overview
The ml-engineering project is an open-source book that serves as a comprehensive guide for machine learning engineers, particularly those working with large language models (LLMs) and multi-modal models. It offers methodologies, tools, and step-by-step instructions for training, fine-tuning, and inference. Unique to this project is its focus on practical, hands-on solutions, with scripts and commands that engineers can directly apply to their work.
Key Features
- Practical Guides: Detailed instructions and scripts for training and inference of large models.
- Technical Insights: In-depth knowledge from the author's experience with models like BLOOM-176B and IDEFICS-80B.
- Resource Compilation: A collection of tools, commands, and methodologies for quick reference and problem-solving.
Use Cases
- LLM/VLM Training Engineers: Engineers who need practical solutions for training large language models.
- Model Operators: Professionals responsible for the operation and optimization of large models in production.
- Research Scientists: Researchers looking for insights and methodologies to advance their work in AI.
Advantages
- Hands-On Approach: Provides ready-to-use scripts and commands for immediate application.
- Community Engagement: Offers a platform for discussions and sharing of experiences in ML engineering.
- Regular Updates: Maintained with the latest insights and tools from the field.
Limitations / Considerations
- License Unknown: The project's license is not specified, which might affect its use in commercial applications.
- Specialized Content: The content is highly technical and may require a strong background in ML engineering to fully utilize.
Similar / Related Projects
- Hugging Face Transformers: A library of pre-trained models and a community for sharing state-of-the-art NLP models. It differs in that it focuses on model sharing rather than training methodologies.
- TensorFlow Extended (TFX): An end-to-end platform for deploying production ML pipelines. It provides a more structured approach to MLOps compared to the ml-engineering project.
- MLflow: An open-source platform for managing the ML lifecycle, including experimentation, reproducibility, and deployment. It offers a broader scope than ml-engineering, covering more aspects of the ML lifecycle.
Basic Information
- GitHub: https://github.com/stas00/ml-engineering
- Stars: 14,328
- License: Unknown
- Last Commit: 2025-07-16
📊 Project Information
- Project Name: ml-engineering
- GitHub URL: https://github.com/stas00/ml-engineering
- Programming Language: Python
- ⭐ Stars: 14,328
- 🍴 Forks: 863
- 📅 Created: 2020-09-02
- 🔄 Last Updated: 2025-07-16
🏷️ Project Topics
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