allenai/open-instruct
Oct 2024 – Jan 2026
4 Months active
Languages Used
PythonyamlYAMLJinjabash
Technical Skills
Configuration ManagementDevOpsModel TrainingShell ScriptingMachine LearningModel Training Configuration
Jacob Morrison
PROFILE
Jacob Morrison
Worked on the allenai/open-instruct repository, delivering features that advanced large-scale model training and evaluation workflows. Developed and refined training scripts, configuration files, and dataset management processes for OLMo and SFT models, supporting both commercial and non-commercial datasets. Leveraged Python, YAML, and shell scripting to implement reproducible pipelines, expand compute resource management, and enhance data preprocessing and evaluation robustness. Integrated Beaker and Wandb for experiment tracking and documentation, improving onboarding and usability. Focused on code refactoring, data engineering, and collaborative documentation updates, the work enabled faster iteration, standardized configurations, and more reliable experimentation for machine learning and natural language processing projects.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
7Total
Bugs
0
Commits
7
Features
6
Lines of code
1,691
Activity Months4
Your Network
62 peopleWork History
January 2026
1 Commits • 1 Features
Jan 1, 2026January 2026 — Repository: allenai/open-instruct. Key feature delivered: OLMo 3 Training Scripts and Documentation. Implemented training scripts for OLMo 3 models: Instruct SFT, Think SFT, Instruct DPO, and Instruct RLVR; added support for 7B and 32B variants. README updated with links to scripts, Beaker and Wandb runs; commits include ff4a9252b7c95555139a2aa04aaad8fb9c6abc75. Impact: improved usability, onboarding, and reproducibility of training workflows. Bugs fixed: none reported this month; focus on feature delivery and documentation. Technologies/skills: scripting for supervised fine-tuning and RL workflows, Beaker/Wandb integrations, documentation-centric development, and collaborative contributions (co-authored commits).
1 Commits • 1 Features
Jan 1, 2026January 2026 — Repository: allenai/open-instruct. Key feature delivered: OLMo 3 Training Scripts and Documentation. Implemented training scripts for OLMo 3 models: Instruct SFT, Think SFT, Instruct DPO, and Instruct RLVR; added support for 7B and 32B variants. README updated with links to scripts, Beaker and Wandb runs; commits include ff4a9252b7c95555139a2aa04aaad8fb9c6abc75. Impact: improved usability, onboarding, and reproducibility of training workflows. Bugs fixed: none reported this month; focus on feature delivery and documentation. Technologies/skills: scripting for supervised fine-tuning and RL workflows, Beaker/Wandb integrations, documentation-centric development, and collaborative contributions (co-authored commits).
January 2026
July 2025
3 Commits • 2 Features
Jul 1, 2025July 2025 performance summary (Month: 2025-07) for repository allenai/open-instruct. Delivered two major features with targeted reliability improvements, plus robustness fixes in data handling and evaluation pipelines. The work enhanced model capabilities, data quality, and reproducibility, driving faster, more trustworthy experimentation and decision-making.
July 2025
3 Commits • 2 Features
Jul 1, 2025July 2025 performance summary (Month: 2025-07) for repository allenai/open-instruct. Delivered two major features with targeted reliability improvements, plus robustness fixes in data handling and evaluation pipelines. The work enhanced model capabilities, data quality, and reproducibility, driving faster, more trustworthy experimentation and decision-making.
November 2024
1 Commits • 1 Features
Nov 1, 2024In 2024-11, delivered the training configuration setup for the v3.9 non-commercial dataset for 70B and 8B models in allenai/open-instruct. The work finalizes the non-commercial configuration (nc) for v3.9, introducing versioned config files that specify model names, dataset mixers, and training parameters, ready for production. No major bugs were fixed this month. This accelerates large-scale training readiness, improves reproducibility, and aligns with the dataset version rollout.
1 Commits • 1 Features
Nov 1, 2024In 2024-11, delivered the training configuration setup for the v3.9 non-commercial dataset for 70B and 8B models in allenai/open-instruct. The work finalizes the non-commercial configuration (nc) for v3.9, introducing versioned config files that specify model names, dataset mixers, and training parameters, ready for production. No major bugs were fixed this month. This accelerates large-scale training readiness, improves reproducibility, and aligns with the dataset version rollout.
November 2024
October 2024
2 Commits • 2 Features
Oct 1, 2024Month 2024-10 focused on expanding evaluation and fine-tuning compute resources and finalizing the v3.8 SFT mix. This included adding new clusters to the default resource lists and updating submit_eval_jobs.py, and completing v3.8 SFT dataset mixtures with new training configurations for 70B and 8B models. These changes improve throughput, reproducibility, and readiness for large-scale experiments, delivering business value through faster iteration, more reliable evaluation pipelines, and standardized configurations.
October 2024
2 Commits • 2 Features
Oct 1, 2024Month 2024-10 focused on expanding evaluation and fine-tuning compute resources and finalizing the v3.8 SFT mix. This included adding new clusters to the default resource lists and updating submit_eval_jobs.py, and completing v3.8 SFT dataset mixtures with new training configurations for 70B and 8B models. These changes improve throughput, reproducibility, and readiness for large-scale experiments, delivering business value through faster iteration, more reliable evaluation pipelines, and standardized configurations.
Activity
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Quality Metrics
Correctness90.0%
Maintainability88.6%
Architecture90.0%
Performance80.0%
AI Usage25.8%
Skills & Technologies
Programming Languages
JinjaPythonYAMLbashyaml
Technical Skills
AI model trainingCode RefactoringConfiguration ManagementData EngineeringData PreprocessingData ProcessingDataset ManagementDevOpsHugging Face TransformersLoggingMachine LearningMachine Learning OperationsModel TrainingModel Training ConfigurationNatural Language Processing
Repositories Contributed To
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