UKGovernmentBEIS/inspect_ai
Apr 2026 – Apr 2026
1 Month active
Languages Used
Python
Technical Skills
API DevelopmentData AnalysisEnd-to-End TestingMachine LearningModel EvaluationPython
Prasad Nirantar
PROFILE
Prasad Nirantar
During April 2026, contributed to the UKGovernmentBEIS/inspect_ai repository by developing a perplexity-based evaluation framework for language models, introducing prompt log-probability support and multiple scoring metrics to enhance benchmarking. The work involved designing a provider-based architecture, adding a new VLLMCompletionsAPI for perplexity evaluation, and implementing comprehensive unit and end-to-end tests to ensure reliability. Python was used extensively alongside skills in API development and machine learning, with a focus on maintainability and scalability. Code organization, logging, and documentation were improved, supporting ongoing model quality assessment and benchmarking across pre-training, classification, code completion, and post-fine-tuning evaluation scenarios.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
2,247
Activity Months1
Your Network
189 peopleShared Repositories
189
Work History
April 2026
2 Commits • 1 Features
Apr 1, 2026Concise monthly summary for 2026-04 focused on business value and technical achievements. Highlights include delivering a perplexity-based evaluation framework for language models with log-probability support, a new vLLM completions provider, architecture refinements, and thorough testing across unit and end-to-end scenarios. The work emphasizes maintainability, scalability, and improved benchmarking capabilities across pre-training benchmarks, classification benchmarks, code completion, and model quality evaluation after fine-tuning/quantization/distillation.
2 Commits • 1 Features
Apr 1, 2026Concise monthly summary for 2026-04 focused on business value and technical achievements. Highlights include delivering a perplexity-based evaluation framework for language models with log-probability support, a new vLLM completions provider, architecture refinements, and thorough testing across unit and end-to-end scenarios. The work emphasizes maintainability, scalability, and improved benchmarking capabilities across pre-training benchmarks, classification benchmarks, code completion, and model quality evaluation after fine-tuning/quantization/distillation.
April 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance80.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
API DevelopmentData AnalysisEnd-to-End TestingMachine LearningModel EvaluationPythonUnit Testing
Repositories Contributed To
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