AI-Hypercomputer/tpu-recipes
Jul 2025 – Oct 2025
2 Months active
Languages Used
MarkdownShell
Technical Skills
Cloud ComputingDeep LearningLLM TrainingMachine LearningTPU TrainingMachine Learning Engineering
Carlos Bustamante Horta
PROFILE
Carlos Bustamante Horta
Carlos Bus developed scalable training recipes for large language models in the AI-Hypercomputer/tpu-recipes repository, focusing on Llama 3.1 and Gemma3-12B models. He engineered end-to-end workflows for training on v6e TPU clusters, introducing shell scripts and Markdown documentation to streamline setup and reproducibility. His work included multi-slice TPU configurations and detailed setup instructions, enabling efficient experimentation and reducing onboarding time for new users. Leveraging skills in cloud computing, deep learning, and machine learning engineering, Carlos delivered robust, production-ready pipelines that improved experimentation throughput and cost efficiency. The solutions demonstrated depth in both technical implementation and user-facing documentation.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
1,205
Activity Months2
Your Network
4437 peopleSame Organization
@google.com4396
Benedict OdaiMember
Craig IngramMember
KayyuriMember
Scott SuarezMember
Agent2Agent (A2A) BotMember
Andreas AbelMember
Aadi KapurMember
Aadish GoelMember
Aahil MehtaMember
Work History
October 2025
1 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for AI-Hypercomputer/tpu-recipes. Delivered targeted feature: Gemma3-12B Training Recipes and Multi-Slice TPU Configuration for v6e TPU instances, with setup instructions, shell scripts for 1/2/4 slices, READMEs, and per-configuration scripts. All changes committed in 6472c996cad7ef60454df09e97e9f032cecba065 with message 'Add recipes for Gemma3-12B on v6e'. Major bugs fixed: none reported. Overall impact: enables reproducible Gemma3-12B training at scale on TPU clusters, reducing onboarding and setup time, improving experimentation throughput and cost efficiency. Technologies/skills demonstrated: TPU v6e, multi-slice training, shell scripting, repository documentation, and Git-based change management.
1 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for AI-Hypercomputer/tpu-recipes. Delivered targeted feature: Gemma3-12B Training Recipes and Multi-Slice TPU Configuration for v6e TPU instances, with setup instructions, shell scripts for 1/2/4 slices, READMEs, and per-configuration scripts. All changes committed in 6472c996cad7ef60454df09e97e9f032cecba065 with message 'Add recipes for Gemma3-12B on v6e'. Major bugs fixed: none reported. Overall impact: enables reproducible Gemma3-12B training at scale on TPU clusters, reducing onboarding and setup time, improving experimentation throughput and cost efficiency. Technologies/skills demonstrated: TPU v6e, multi-slice training, shell scripting, repository documentation, and Git-based change management.
October 2025
July 2025
1 Commits • 1 Features
Jul 1, 2025In 2025-07, delivered core capability to train Llama 3.1 models on TPU clusters by adding new training recipes for 8B/27B configurations across small v6e TPU clusters. Updated README and shell scripts to reflect latest dependencies, model configurations, and end-to-end training steps, enabling reproducible and scalable training pipelines on targeted TPU hardware. This work improves onboarding, accelerates experiments, and provides a solid foundation for production-ready Llama 3.1 training workflows.
July 2025
1 Commits • 1 Features
Jul 1, 2025In 2025-07, delivered core capability to train Llama 3.1 models on TPU clusters by adding new training recipes for 8B/27B configurations across small v6e TPU clusters. Updated README and shell scripts to reflect latest dependencies, model configurations, and end-to-end training steps, enabling reproducible and scalable training pipelines on targeted TPU hardware. This work improves onboarding, accelerates experiments, and provides a solid foundation for production-ready Llama 3.1 training workflows.
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
MarkdownShell
Technical Skills
Cloud ComputingDeep LearningLLM TrainingMachine LearningMachine Learning EngineeringTPU Training
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