rasbt/llms-from-scratch
Jan 2025 – Apr 2025
2 Months active
Languages Used
Python
Technical Skills
Pythondata visualizationmachine learningJupyter Notebookdata science
Henry Shi
PROFILE
Henry Shi
Worked on the rasbt/llms-from-scratch repository, focusing on enhancing model training interpretability and experiment reproducibility. Developed an embedding visualization feature by integrating print statements into the training loop, allowing users to observe embedding structures and values in real time with minimal performance overhead. Provided user-facing notes to support interpretability and debugging. Later, improved the reliability of coursework by enabling deterministic execution for a specific exercise and correcting the notebook JSON version to ensure compatibility across environments. All work was implemented using Python, Jupyter Notebook, and data science tools, emphasizing reproducibility, transparency, and ease of use for learners and developers.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
2
Lines of code
48
Activity Months2
Your Network
12 peopleShared Repositories
12
Work History
April 2025
2 Commits • 1 Features
Apr 1, 2025In April 2025, delivered reproducibility and compatibility improvements in rasbt/llms-from-scratch by adding deterministic execution for Exercise 5.3 and fixing the notebook JSON version. These changes enhance reliability of experiments and reduce setup friction for learners and automation pipelines, aligning with the project’s goal of robust, reproducible coursework experiences.
2 Commits • 1 Features
Apr 1, 2025In April 2025, delivered reproducibility and compatibility improvements in rasbt/llms-from-scratch by adding deterministic execution for Exercise 5.3 and fixing the notebook JSON version. These changes enhance reliability of experiments and reduce setup friction for learners and automation pipelines, aligning with the project’s goal of robust, reproducible coursework experiences.
April 2025
January 2025
2 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for rasbt/llms-from-scratch: Delivered embedding visualization during model training by adding print statements to surface embeddings, enhancing interpretability and debugging during training. Implemented across two commits to surface embedding structure and values with minimal overhead, improving training observability and user understanding of embedding dynamics.
January 2025
2 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for rasbt/llms-from-scratch: Delivered embedding visualization during model training by adding print statements to surface embeddings, enhancing interpretability and debugging during training. Implemented across two commits to surface embedding structure and values with minimal overhead, improving training observability and user understanding of embedding dynamics.
Activity
Loading activity data...
Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage50.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Jupyter NotebookPythondata sciencedata visualizationmachine learning
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline