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Shifani Rajabose

PROFILE

Shifani Rajabose

Shifani Rajabose developed and integrated advanced model support and evaluation features across the huggingface/optimum-habana and opea-project repositories. She enabled DeepSeek-V3 model compatibility and introduced a system instruction parameter for controlled language model evaluation, enhancing reproducibility and usability for Habana hardware users. Her work included Docker Compose-based Milvus vector database integration for GenAIExamples, along with documentation and configuration updates to streamline onboarding and deployment. Using Python, Docker, and YAML, Shifani focused on maintainable code, clear documentation, and robust testing. Her contributions addressed real-world deployment challenges and improved the flexibility and clarity of AI model workflows for end users.

Overall Statistics

Feature vs Bugs

78%Features

Repository Contributions

9Total
Bugs
2
Commits
9
Features
7
Lines of code
7,218
Activity Months5

Work History

September 2025

1 Commits • 1 Features

Sep 1, 2025

September 2025 monthly summary for developer work on huggingface/optimum-habana. Key focus: improve lm_eval usage guidance by documenting the --system_instruction option, with a concrete Moonlight-16B-A3B model usage example on the mmlu_abstract_algebra task, and clarifying its effect on model behavior. Delivered via a single commit updating the README to align documentation with evaluation workflow. This work enhances onboarding, reduces misconfiguration risk, and improves reproducibility for end users evaluating system instructions on Habana-enabled deployments. No major bugs fixed this month; efforts centered on documentation quality and developer experience.

August 2025

1 Commits • 1 Features

Aug 1, 2025

In August 2025, delivered System Instruction Support for the lm_eval script in the huggingface/optimum-habana repository. A new --system_instruction CLI argument allows users to supply a system prompt, which is passed to evaluator.simple_evaluate to enable more controlled and context-aware language model evaluations on Habana hardware. This enhancement improves evaluation fidelity, repeatability, and usability for researchers analyzing Habana-backed models.

April 2025

2 Commits • 2 Features

Apr 1, 2025

April 2025 monthly summary for developer work across opea-project/docs and opea-project/GenAIExamples. Key focus areas: documentation cleanup for vector database setup and enabling Milvus as an optional vector DB for MultimodalQnA, with related Docker Compose configurations, docs, and validation test scripts. No critical defects identified this month; work delivered enhances onboarding, flexibility, and testability, supporting faster feature delivery and broader adoption.

March 2025

4 Commits • 2 Features

Mar 1, 2025

March 2025 monthly summary focusing on key accomplishments, major fixes, and business impact across the GenAIExamples and docs repositories. Highlights include Milvus as a new vector DB option for DocIndexRetriever with Docker Compose and environment-specific tests; targeted README and documentation fixes that clarified hardware compatibility and deployment guidance; and visual/UX improvements to the docs site. Demonstrated cross-environment testing, Docker Compose configurations, and improved onboarding clarity.

February 2025

1 Commits • 1 Features

Feb 1, 2025

February 2025 (2025-02) Monthly summary focusing on business value and technical achievements. Key features delivered: - DeepSeek-V3 model support added to the Optimum-Habana library, including new configurations, implementations, and updated documentation to reflect compatibility with the model. Commit: c5a715c3cea9c51e983e0c2bef468ed47b2b3217. Major bugs fixed: - None reported. The month focused on feature integration and documentation to ensure smooth DeepSeek-V3 deployment on Habana hardware. Overall impact and accomplishments: - Expanded Habana-Optimum integration by enabling DeepSeek-V3, accelerating deployment of cutting-edge models on Habana accelerators. - Improved maintainability and usability through updated docs and configurations, reducing setup time for users. Technologies/skills demonstrated: - Python, deep learning model integration, Optimum-Habana library development, documentation, and code review processes.

Activity

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Quality Metrics

Correctness96.8%
Maintainability96.8%
Architecture95.6%
Performance90.0%
AI Usage20.0%

Skills & Technologies

Programming Languages

BashMarkdownPythonYAML

Technical Skills

Command-line InterfaceConfiguration ManagementDockerDocker ComposeDocumentationFront end developmentFull Stack DevelopmentHPU OptimizationMilvusModel EvaluationModel IntegrationNatural Language ProcessingShell ScriptingSystem IntegrationTesting

Repositories Contributed To

4 repos

Overview of all repositories you've contributed to across your timeline

opea-project/GenAIExamples

Mar 2025 Apr 2025
2 Months active

Languages Used

BashMarkdownPythonYAML

Technical Skills

Configuration ManagementDockerDocumentationMilvusShell ScriptingVector Databases

opea-project/docs

Mar 2025 Apr 2025
2 Months active

Languages Used

Markdown

Technical Skills

DocumentationFront end development

huggingface/optimum-habana

Aug 2025 Sep 2025
2 Months active

Languages Used

PythonMarkdown

Technical Skills

Command-line InterfaceModel EvaluationNatural Language ProcessingDocumentation

HabanaAI/optimum-habana-fork

Feb 2025 Feb 2025
1 Month active

Languages Used

MarkdownPython

Technical Skills

Full Stack DevelopmentHPU OptimizationModel IntegrationTransformers Library