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Andrej Gobeljić

PROFILE

Andrej Gobeljić

Over three months, this developer enhanced model training and testing workflows across tenstorrent/tt-forge-models, tt-xla, and tt-mlir repositories. They integrated LoRA adapters for efficient fine-tuning, improved hardware-aware test automation, and expanded support for causal language models using Python and PyTorch. Their work included debugging and resolving training failures, automating triage for missing inputs and dtype mismatches, and optimizing matrix operations through MLIR-based fusion patterns. By refining YAML-based test configurations and stabilizing evaluation pipelines, they reduced flakiness and accelerated iteration cycles. The developer’s contributions improved reliability, performance, and cross-hardware compatibility in machine learning model development and deployment.

Overall Statistics

Feature vs Bugs

64%Features

Repository Contributions

19Total
Bugs
4
Commits
19
Features
7
Lines of code
4,681
Activity Months3

Work History

June 2026

5 Commits • 3 Features

Jun 1, 2026

June 2026 monthly summary for developer work across three cores: reliability of training/test pipelines, automatic triage of missing-input failures, and performance-oriented optimizations. Cross-repo collaboration delivered concrete, business-value features and fixes that reduce flakiness, accelerate iteration cycles, and improve runtime efficiency.

May 2026

11 Commits • 2 Features

May 1, 2026

May 2026 monthly summary focusing on business value and technical achievements across tt-xla and tt-forge-models. This month included delivery of major testing framework enhancements, LoRA-enabled model support, critical reliability fixes in training pipelines, and comprehensive YAML/test-status updates that improve visibility and triage efficiency. The work enabled faster iteration cycles on Tenstorrent hardware and strengthened the end-to-end training and evaluation workflow.

April 2026

3 Commits • 2 Features

Apr 1, 2026

April 2026 monthly summary: Key features delivered and critical fixes across two repositories (tt-forge-models and tt-xla) aimed at expanding fine-tuning capabilities, improving stability, and aligning testing with hardware capabilities to drive business value.

Activity

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Quality Metrics

Correctness88.4%
Maintainability82.0%
Architecture84.2%
Performance83.2%
AI Usage46.2%

Skills & Technologies

Programming Languages

C++PythonYAML

Technical Skills

Data ProcessingDebuggingDeep LearningError HandlingMLIRMachine LearningModel OptimizationModel TrainingPyTorchPythonPython DevelopmentPython ProgrammingTest AutomationTestingcompiler design

Repositories Contributed To

3 repos

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

tenstorrent/tt-xla

Apr 2026 Jun 2026
3 Months active

Languages Used

YAMLPython

Technical Skills

PyTorchPythonhardware compatibility testingtest automationDebuggingError Handling

tenstorrent/tt-forge-models

Apr 2026 Jun 2026
3 Months active

Languages Used

Python

Technical Skills

Deep LearningMachine LearningModel OptimizationPyTorchPython DevelopmentData Processing

tenstorrent/tt-mlir

Jun 2026 Jun 2026
1 Month active

Languages Used

C++Python

Technical Skills

MLIRcompiler designmachine learningperformance optimization