aws-samples/awsome-distributed-training
Mar 2026 – Mar 2026
1 Month active
Languages Used
PythonShellYAML
Technical Skills
Deep LearningDockerKubernetesMachine LearningNatural Language Processing
Mijanur Palash
PROFILE
Mijanur Palash
During March 2026, Mijanur developed an end-to-end multilingual fine-tuning capability for the GPT-OSS 20B model in the aws-samples/awsome-distributed-training repository. He implemented a LoRA and GRPO-based finetuning recipe, complete with training scripts, Docker configurations, and evaluation pipelines, enabling scalable and reproducible deployments on Kubernetes. His work included reorganizing repository manifests, addressing reliability and compatibility issues in Python code, and hardening the environment for production by refining dependency management and build processes. By leveraging deep learning and natural language processing expertise, Mijanur delivered a robust, maintainable solution that improved multilingual model performance and deployment reliability for distributed training.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
2,972
Activity Months1
Work History
March 2026
1 Commits • 1 Features
Mar 1, 2026Month: 2026-03 — End-to-end multilingual fine-tuning capability delivered for GPT-OSS 20B using LoRA and GRPO, plus stability and maintainability improvements in aws-samples/awsome-distributed-training. Achievements include a complete finetuning setup with training scripts, Docker configurations, and evaluation mechanisms; repo reorganization for scalable deployment; critical fixes for reliability and compatibility; and environment hardening for production readiness.
1 Commits • 1 Features
Mar 1, 2026Month: 2026-03 — End-to-end multilingual fine-tuning capability delivered for GPT-OSS 20B using LoRA and GRPO, plus stability and maintainability improvements in aws-samples/awsome-distributed-training. Achievements include a complete finetuning setup with training scripts, Docker configurations, and evaluation mechanisms; repo reorganization for scalable deployment; critical fixes for reliability and compatibility; and environment hardening for production readiness.
March 2026
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
PythonShellYAML
Technical Skills
Deep LearningDockerKubernetesMachine LearningNatural Language Processing
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