--- title: "olmo-eval vs instruct-eval" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/allenai-olmo-eval-vs-declare-lab-instruct-eval" tools: ["allenai-olmo-eval", "declare-lab-instruct-eval"] --- # olmo-eval vs instruct-eval *GraphCanon updated Aug 7, 2026* ## Verdict Pick olmo-eval if olmo-eval is an evaluation framework for large language models, using uv for reproducible builds. It focuses on modular task implementations and integrates with various datasets via defined tasks; pick instruct-eval if key facts about instruct-eval. [olmo-eval](https://github.com/allenai/olmo-eval) reports 65 GitHub stars, 14 forks, and 38 open issues, last pushed Aug 6, 2026. [instruct-eval](https://declare-lab.github.io/instruct-eval/) has 552 stars, 45 forks, and 24 open issues, last pushed Mar 10, 2024. Figures are from public GitHub metadata via [olmo-eval's repository](https://github.com/allenai/olmo-eval) and [instruct-eval's repository](https://github.com/declare-lab/instruct-eval). | | [olmo-eval](/tools/allenai-olmo-eval.md) | [instruct-eval](/tools/declare-lab-instruct-eval.md) | | --- | --- | --- | | Tagline | Olmo Evaluation Framework for LLM Tasks | Quantitative evaluation for instruction-tuned language models | | Stars | 65 | 552 | | Forks | 14 | 45 | | Open issues | 38 | 24 | | Language | Python | Python | | Adopt for | Olmo-eval is an evaluation framework for large language models, using uv for reproducible builds. It focuses on modular task implementations and integrates with various datasets via defined tasks. | Key facts about instruct-eval | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | The tool is distributed under Apache-2.0 license | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [olmo-eval](/tools/allenai-olmo-eval.md) | [instruct-eval](/tools/declare-lab-instruct-eval.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 879d | | Open issues (now) | 38 | 24 | | Full report | [trust report](/tools/allenai-olmo-eval/trust.md) | [trust report](/tools/declare-lab-instruct-eval/trust.md) | ## Decision facts: olmo-eval - **Adopt for:** Olmo-eval is an evaluation framework for large language models, using uv for reproducible builds. It focuses on modular task implementations and integrates with various datasets via defined tasks. ## Decision facts: instruct-eval - **Requirements:** Min 8 GB RAM; Requires Python environment setup and specific dependencies as outlined in the repository's documentation. - **Adopt for:** Key facts about instruct-eval - **License detail:** The tool is distributed under Apache-2.0 license ## Choose when ### Choose olmo-eval if… - Tags unique to olmo-eval: datasets, python, tasks, uv. - olmo-eval ships Docker support for self-hosted deployment. - When you need a flexible evaluation setup that works with a variety of LLMs and datasets. ### Choose instruct-eval if… - Requirements: Min 8 GB RAM; Requires Python environment setup and specific dependencies as outlined in the repository's documentation.. - Tags unique to instruct-eval: benchmarking, instruct-tuning, safety. - When you need to quantitatively evaluate the performance of instruction-tuned large language models such as Alpaca and Flan-T5 on held-out tasks. ## When NOT to use olmo-eval - When you require a simpler setup that doesn't need the reproducibility constraints of uv builds. - If your project already has an established evaluation toolchain and does not benefit from introducing a new framework for manageability reasons. ## When NOT to use instruct-eval - When primarily interested in general model evaluation without a focus on instruction-tuned LMs. - If your primary interest lies in qualitative assessment rather than quantitative metrics. - If you need support for non-HuggingFace Transformer models, as instruct-eval mainly supports models from the HuggingFace ecosystem. ## Common questions ### What is the difference between olmo-eval and instruct-eval? olmo-eval: Olmo Evaluation Framework for LLM Tasks. instruct-eval: Quantitative evaluation for instruction-tuned language models. See the comparison table for live GitHub stats and shared categories. ### When should I choose olmo-eval over instruct-eval? Choose olmo-eval over instruct-eval when Tags unique to olmo-eval: datasets, python, tasks, uv; olmo-eval ships Docker support for self-hosted deployment; When you need a flexible evaluation setup that works with a variety of LLMs and datasets. ### When should I choose instruct-eval over olmo-eval? Choose instruct-eval over olmo-eval when Requirements: Min 8 GB RAM; Requires Python environment setup and specific dependencies as outlined in the repository's documentation.; Tags unique to instruct-eval: benchmarking, instruct-tuning, safety; When you need to quantitatively evaluate the performance of instruction-tuned large language models such as Alpaca and Flan-T5 on held-out tasks. ### When should I avoid olmo-eval? When you require a simpler setup that doesn't need the reproducibility constraints of uv builds. If your project already has an established evaluation toolchain and does not benefit from introducing a new framework for manageability reasons. ### When should I avoid instruct-eval? When primarily interested in general model evaluation without a focus on instruction-tuned LMs. If your primary interest lies in qualitative assessment rather than quantitative metrics. If you need support for non-HuggingFace Transformer models, as instruct-eval mainly supports models from the HuggingFace ecosystem. ### Is olmo-eval or instruct-eval more popular on GitHub? instruct-eval has more GitHub stars (552 vs 65). Stars measure visibility, not whether either tool fits your constraints. ### Are olmo-eval and instruct-eval open source? Yes - both are open-source projects on GitHub (olmo-eval: Apache-2.0, instruct-eval: Apache-2.0). ### Where can I find alternatives to olmo-eval or instruct-eval? GraphCanon lists graph-backed alternatives at [olmo-eval alternatives](/tools/allenai-olmo-eval/alternatives) and [instruct-eval alternatives](/tools/declare-lab-instruct-eval/alternatives) ([olmo-eval markdown twin](/tools/allenai-olmo-eval/alternatives.md), [instruct-eval markdown twin](/tools/declare-lab-instruct-eval/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/allenai-olmo-eval-vs-declare-lab-instruct-eval.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, olmo-eval or instruct-eval? olmo-eval: Very active. instruct-eval: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for olmo-eval and instruct-eval? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [olmo-eval trust report](/tools/allenai-olmo-eval/trust); [instruct-eval trust report](/tools/declare-lab-instruct-eval/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=allenai-olmo-eval`](/api/graphcanon/graph?tool=allenai-olmo-eval) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_