tenstorrent/tt-inference-server
Oct 2025 – Jan 2026
4 Months active
Languages Used
PythonDockerfile
Technical Skills
Python scriptingdata evaluationAPI integrationDevOpsMachine LearningModel Optimization
Marko Jeremic
PROFILE
Marko Jeremic
Milan Jeremic contributed to the tenstorrent/tt-inference-server repository by developing and refining backend features that improved model deployment, evaluation, and reporting workflows. Over four months, he upgraded deployment environments, introduced new model configurations, and enhanced system reliability through Python scripting, Docker containerization, and DevOps practices. Milan implemented robust metadata validation, streamlined packaging with uv, and improved test reporting for better traceability. His work included aligning benchmarking references with current research, supporting new model types, and hardening runtime environments. The depth of his contributions is reflected in well-documented, maintainable code that increased deployment flexibility, observability, and operational stability across the platform.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
13Total
Bugs
0
Commits
13
Features
7
Lines of code
680
Activity Months4
Your Network
379 peopleWork History
January 2026
6 Commits • 2 Features
Jan 1, 2026January 2026 focused on stabilizing and accelerating the tt-inference-server deployment, improving testing visibility, and laying groundwork for scalable operations. Deliveries centered on media inference server deployment and packaging improvements, Kubernetes? actually uv packaging, Dockerfile/runtime hardening, and enhanced test reporting and data organization, resulting in faster deployments, more reliable runtimes, and clearer test traceability.
6 Commits • 2 Features
Jan 1, 2026January 2026 focused on stabilizing and accelerating the tt-inference-server deployment, improving testing visibility, and laying groundwork for scalable operations. Deliveries centered on media inference server deployment and packaging improvements, Kubernetes? actually uv packaging, Dockerfile/runtime hardening, and enhanced test reporting and data organization, resulting in faster deployments, more reliable runtimes, and clearer test traceability.
January 2026
December 2025
3 Commits • 2 Features
Dec 1, 2025December 2025 focused on strengthening model metadata and reporting for tenstorrent/tt-inference-server. Key features delivered include a new ModelSource enum with a 'noaction' option and an InferenceEngine property added to model metadata to differentiate model types. Expanded tests and runtime validations ensure correctness, including Forge model support in run.py. Enhanced SDXL image model support in summary reports with a refactored, faster reporting pipeline. Fixed critical test failures and stabilized runners, improving overall reliability. These changes deliver greater deployment flexibility, improved model validation, and more efficient, accurate reporting.
December 2025
3 Commits • 2 Features
Dec 1, 2025December 2025 focused on strengthening model metadata and reporting for tenstorrent/tt-inference-server. Key features delivered include a new ModelSource enum with a 'noaction' option and an InferenceEngine property added to model metadata to differentiate model types. Expanded tests and runtime validations ensure correctness, including Forge model support in run.py. Enhanced SDXL image model support in summary reports with a refactored, faster reporting pipeline. Fixed critical test failures and stabilized runners, improving overall reliability. These changes deliver greater deployment flexibility, improved model validation, and more efficient, accurate reporting.
November 2025
3 Commits • 2 Features
Nov 1, 2025November 2025 highlights for tenstorrent/tt-inference-server focused on deployment readiness, reliability, and observability. Key changes include upgrading the deployment environment to Python 3.11 with Forge optimizer updates and introducing new model configurations via ModelSpecTemplates and EvalConfigs for forge models (resnet, mobilnet, vovnet). In addition, reliability and observability were strengthened through a health-check retry mechanism, a startup wait to ensure liveness, and standardized log naming for LLM and media components, plus a bug fix to correct the media server log filename. These efforts reduce deployment risk, accelerate model experimentation, and improve monitoring and incident response.
3 Commits • 2 Features
Nov 1, 2025November 2025 highlights for tenstorrent/tt-inference-server focused on deployment readiness, reliability, and observability. Key changes include upgrading the deployment environment to Python 3.11 with Forge optimizer updates and introducing new model configurations via ModelSpecTemplates and EvalConfigs for forge models (resnet, mobilnet, vovnet). In addition, reliability and observability were strengthened through a health-check retry mechanism, a startup wait to ensure liveness, and standardized log naming for LLM and media components, plus a bug fix to correct the media server log filename. These efforts reduce deployment risk, accelerate model experimentation, and improve monitoring and incident response.
November 2025
October 2025
1 Commits • 1 Features
Oct 1, 2025October 2025 (2025-10) delivered a targeted alignment of Qwen evaluation references in the tt-inference-server to ensure benchmarking remains consistent with the latest research findings. The primary accomplishment was updating the published_score_ref for Qwens to point to the new blog post, ensuring evaluation metrics reflect current literature. This change was implemented in tenstorrent/tt-inference-server with commit 5cde7fa6b9191cd87dadf3c7df0dd0fe9e3e2225 (PR #1047). No major bugs were fixed this month in this repository. Overall impact includes more reliable benchmarks, improved credibility of model comparisons, and smoother decision-making for model selection. Demonstrated technologies/skills include Git-based development, metric configuration management, and collaboration with researchers to keep references up-to-date.
October 2025
1 Commits • 1 Features
Oct 1, 2025October 2025 (2025-10) delivered a targeted alignment of Qwen evaluation references in the tt-inference-server to ensure benchmarking remains consistent with the latest research findings. The primary accomplishment was updating the published_score_ref for Qwens to point to the new blog post, ensuring evaluation metrics reflect current literature. This change was implemented in tenstorrent/tt-inference-server with commit 5cde7fa6b9191cd87dadf3c7df0dd0fe9e3e2225 (PR #1047). No major bugs were fixed this month in this repository. Overall impact includes more reliable benchmarks, improved credibility of model comparisons, and smoother decision-making for model selection. Demonstrated technologies/skills include Git-based development, metric configuration management, and collaboration with researchers to keep references up-to-date.
Activity
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Quality Metrics
Correctness86.2%
Maintainability84.6%
Architecture84.6%
Performance81.6%
AI Usage27.6%
Skills & Technologies
Programming Languages
DockerfilePython
Technical Skills
API developmentAPI integrationContainerizationDevOpsDockerMachine LearningModel OptimizationPythonPython DevelopmentPython scriptingVirtual Environmentsbackend developmentdata evaluationdata processingdata validation
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