meta-llama/llama-recipes
Sep 2025 – Nov 2025
3 Months active
Languages Used
MarkdownPythonYAMLplaintexttext
Technical Skills
Data PreparationData ProcessingDataset PreparationMachine LearningModel EvaluationModel Fine-tuning
Beto de Paola
PROFILE
Beto De Paola
Worked on the meta-llama/llama-recipes repository to develop and refine pipelines for fine-tuning and evaluating vision–language models, focusing on synthetic W-2 tax form data. Built end-to-end workflows for model training, benchmarking, and evaluation, introducing memory optimizations and compatibility with external evaluation frameworks. Enhanced repository hygiene by updating .gitignore files and expanding spellcheck wordlists, while improving documentation to streamline onboarding and reproducibility. Used Python, YAML, and Markdown to implement configuration management, data preparation, and technical writing. Prioritized clarity and maintainability in both code and documentation, enabling scalable model evaluation and reducing friction for new contributors and future updates.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
13Total
Bugs
0
Commits
13
Features
4
Lines of code
2,147
Activity Months3
Your Network
2845 peopleSame Organization
@meta.com2798
Peter RongMember
Zain RizviMember
Aahan AggarwalMember
Aliaksei AndreyeuMember
Arjun ChaturvediMember
Aaron PollackMember
Aaryaman SagarMember
Aashay GaikwadMember
Ajanthan AsogamoorthyMember
Work History
November 2025
2 Commits • 1 Features
Nov 1, 2025In 2025-11, delivered targeted documentation improvements and spellcheck enhancements in the meta-llama/llama-recipes repository, strengthening onboarding for the fine-tuning vision model tutorial and increasing the reliability of text processing in recipe workflows. The work reduced ambiguity in guidance and improved spellchecking coverage for common terms.
2 Commits • 1 Features
Nov 1, 2025In 2025-11, delivered targeted documentation improvements and spellcheck enhancements in the meta-llama/llama-recipes repository, strengthening onboarding for the fine-tuning vision model tutorial and increasing the reliability of text processing in recipe workflows. The work reduced ambiguity in guidance and improved spellchecking coverage for common terms.
November 2025
October 2025
8 Commits • 2 Features
Oct 1, 2025October 2025 highlights for meta-llama/llama-recipes: Delivered an end-to-end Vision model fine-tuning, evaluation, and benchmarking pipeline for Llama 3.2 11B Vision with memory-usage optimizations and updated dependencies; enhanced README results and benchmark accuracy reporting; improved logging and compatibility with the Together evaluation framework. Strengthened repository hygiene and documentation quality: added .gitignore for outputs and expanded spellcheck wordlists. Fixed critical issues in benchmarks, including correcting percentage reporting in the custom benchmark and logger level defaults to improve reliability.
October 2025
8 Commits • 2 Features
Oct 1, 2025October 2025 highlights for meta-llama/llama-recipes: Delivered an end-to-end Vision model fine-tuning, evaluation, and benchmarking pipeline for Llama 3.2 11B Vision with memory-usage optimizations and updated dependencies; enhanced README results and benchmark accuracy reporting; improved logging and compatibility with the Together evaluation framework. Strengthened repository hygiene and documentation quality: added .gitignore for outputs and expanded spellcheck wordlists. Fixed critical issues in benchmarks, including correcting percentage reporting in the custom benchmark and logger level defaults to improve reliability.
September 2025
3 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for meta-llama/llama-recipes: Delivered foundational W2 finetuning setup for a vision–language model, including training tooling, configuration files, and evaluation scripts. Implemented a decoder-freeze strategy during training to assess stability and potential performance gains. Created a comprehensive README to document setup, training, and evaluation processes, enabling reproducibility and faster onboarding. This work establishes the groundwork for domain-specific document understanding and scalable evaluation pipelines on synthetic tax-form data.
3 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for meta-llama/llama-recipes: Delivered foundational W2 finetuning setup for a vision–language model, including training tooling, configuration files, and evaluation scripts. Implemented a decoder-freeze strategy during training to assess stability and potential performance gains. Created a comprehensive README to document setup, training, and evaluation processes, enabling reproducibility and faster onboarding. This work establishes the groundwork for domain-specific document understanding and scalable evaluation pipelines on synthetic tax-form data.
September 2025
Activity
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Quality Metrics
Correctness95.4%
Maintainability94.0%
Architecture95.4%
Performance94.0%
AI Usage43.2%
Skills & Technologies
Programming Languages
MarkdownPythonYAMLplaintexttext
Technical Skills
Data AnalysisData ExtractionData PreparationData ProcessingDataset PreparationDocumentationFine-tuningMachine LearningModel EvaluationModel Fine-tuningPython DevelopmentPython ProgrammingPython Scriptingconfiguration managementdata analysis
Repositories Contributed To
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