meta-llama/llama-cookbook
May 2025 – May 2025
1 Month active
Languages Used
JSONJupyter NotebookMarkdownPythonText
Technical Skills
ConfigurationData AnalysisData GenerationDocumentationEvaluation MetricsLLM Integration
agunapal
PROFILE
Agunapal
Agunapal developed an end-to-end Synthetic Data Benchmark Evaluation workflow for the meta-llama/llama-cookbook repository, focusing on generating and evaluating synthetic datasets for LLM benchmarking. Using Python and Jupyter Notebook, Agunapal integrated the Synthetic Data Vault library to create tabular data, then designed a notebook that guides users through context generation, summarization, and factual accuracy checks with Llama-3.3-70B-Instruct. The work included prompt engineering, evaluation metrics, and technical writing to document each step. By updating the README and adding a workflow diagram, Agunapal improved reproducibility and clarity, delivering a well-documented, maintainable solution for synthetic data evaluation.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
1
Lines of code
1,788
Activity Months1
Your Network
3 peopleSame Organization
@ischool.berkeley.edu1
Shared Repositories
2
Work History
May 2025
5 Commits • 1 Features
May 1, 2025May 2025 monthly summary for meta-llama/llama-cookbook: Delivered an end-to-end Synthetic Data Benchmark Evaluation workflow, including a new notebook and documentation for generating evaluation datasets with synthetic data, evaluating hallucinations against ground truth, and guiding context and summarization using Llama-3.3-70B-Instruct. Implemented Synthetic Data Vault-based data generation, added a workflow diagram, and updated the README. Performed spellcheck corrections and configuration updates to improve documentation quality and maintainability.
5 Commits • 1 Features
May 1, 2025May 2025 monthly summary for meta-llama/llama-cookbook: Delivered an end-to-end Synthetic Data Benchmark Evaluation workflow, including a new notebook and documentation for generating evaluation datasets with synthetic data, evaluating hallucinations against ground truth, and guiding context and summarization using Llama-3.3-70B-Instruct. Implemented Synthetic Data Vault-based data generation, added a workflow diagram, and updated the README. Performed spellcheck corrections and configuration updates to improve documentation quality and maintainability.
May 2025
Activity
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Quality Metrics
Correctness98.0%
Maintainability96.0%
Architecture98.0%
Performance96.0%
AI Usage50.0%
Skills & Technologies
Programming Languages
JSONJupyter NotebookMarkdownPythonText
Technical Skills
ConfigurationData AnalysisData GenerationDocumentationEvaluation MetricsLLM IntegrationNotebook DevelopmentPrompt EngineeringSynthetic Data GenerationTechnical Writing
Repositories Contributed To
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