Comparison
instruct-eval vs lm-evaluation-harness
Verdict
Pick instruct-eval if key facts about instruct-eval; pick lm-evaluation-harness if lm-evaluation-harness is a Python framework for evaluating language models in various parallelism modes using different checkpoint formats, compatible with the Megatron-LM backend.
Markdown twin · instruct-eval alternatives · lm-evaluation-harness alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | instruct-eval | lm-evaluation-harness |
|---|---|---|
| Maintenance | Dormant (879d since push) As of 2w · github_public_v1 | Active (24d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | Published findings As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- instruct-eval
- Quantitative evaluation for instruction-tuned language models
- lm-evaluation-harness
- A framework for few-shot evaluation of language models.
Stars
- instruct-eval
- 552
- lm-evaluation-harness
- 14k
Forks
- instruct-eval
- 45
- lm-evaluation-harness
- 3.5k
Open issues
- instruct-eval
- 24
- lm-evaluation-harness
- 938
Language
- instruct-eval
- Python
- lm-evaluation-harness
- Python
Adopt for
- instruct-eval
- Key facts about instruct-eval
- lm-evaluation-harness
- lm-evaluation-harness is a Python framework for evaluating language models in various parallelism modes using different checkpoint formats, compatible with the Megatron-LM backend.
Persona
- instruct-eval
- -
- lm-evaluation-harness
- -
Runtime
- instruct-eval
- -
- lm-evaluation-harness
- -
License
- instruct-eval
- The tool is distributed under Apache-2.0 license
- lm-evaluation-harness
- MIT
Last pushed
- instruct-eval
- Mar 10, 2024
- lm-evaluation-harness
- Jul 13, 2026
Categories
- instruct-eval
- Evaluation & Observability
- lm-evaluation-harness
- Evaluation & Observability
Trust and health
Maintenance
- instruct-eval
- Dormant (18%)
- lm-evaluation-harness
- Active (82%)
Days since push
- instruct-eval
- 879d
- lm-evaluation-harness
- 24d
Open issues (now)
- instruct-eval
- 24
- lm-evaluation-harness
- 938
OSV dependency advisories
- instruct-eval
- Published findings
- lm-evaluation-harness
- No lockfile (source not queried)
Full report
- instruct-eval
- Trust report
- lm-evaluation-harness
- Trust report
Choose instruct-eval if…
- License: instruct-eval is Apache-2.0, lm-evaluation-harness is MIT.
- 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, evaluation, instruct-tuning, llm.
- 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 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.
Choose lm-evaluation-harness if…
- License: lm-evaluation-harness is MIT, instruct-eval is Apache-2.0.
- Tags unique to lm-evaluation-harness: data-parallelism, evaluation-framework, expert-parallelism, language-model.
- - When you need to evaluate large language models across multiple GPUs in data or tensor parallel configurations.
When NOT to use lm-evaluation-harness
- - If your evaluation setup requires pipeline parallelism not currently supported by this framework.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (declare-lab/instruct-eval) · observed Aug 7, 2026
- GitHub forks (declare-lab/instruct-eval) · observed Aug 7, 2026
- Last push (declare-lab/instruct-eval) · observed Mar 10, 2024
- License file (Apache-2.0) · observed Aug 7, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (EleutherAI/lm-evaluation-harness) · observed Aug 7, 2026
- GitHub forks (EleutherAI/lm-evaluation-harness) · observed Aug 7, 2026
- Last push (EleutherAI/lm-evaluation-harness) · observed Jul 13, 2026
- License file (MIT) · observed Aug 7, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: instruct-eval 552 · lm-evaluation-harness 14k (synced Aug 7, 2026).
Common questions
- What is the difference between instruct-eval and lm-evaluation-harness?
- instruct-eval: Quantitative evaluation for instruction-tuned language models. lm-evaluation-harness: A framework for few-shot evaluation of language models.. See the comparison table for live GitHub stats and shared categories.
- When should I choose instruct-eval over lm-evaluation-harness?
- Choose instruct-eval over lm-evaluation-harness when License: instruct-eval is Apache-2.0, lm-evaluation-harness is MIT; 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, evaluation, instruct-tuning, llm; 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 choose lm-evaluation-harness over instruct-eval?
- Choose lm-evaluation-harness over instruct-eval when License: lm-evaluation-harness is MIT, instruct-eval is Apache-2.0; Tags unique to lm-evaluation-harness: data-parallelism, evaluation-framework, expert-parallelism, language-model; - When you need to evaluate large language models across multiple GPUs in data or tensor parallel configurations.
- 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.
- When should I avoid lm-evaluation-harness?
- - If your evaluation setup requires pipeline parallelism not currently supported by this framework.
- Is instruct-eval or lm-evaluation-harness more popular on GitHub?
- lm-evaluation-harness has more GitHub stars (13,560 vs 552). Stars measure visibility, not whether either tool fits your constraints.
- Are instruct-eval and lm-evaluation-harness open source?
- Yes - both are open-source projects on GitHub (instruct-eval: Apache-2.0, lm-evaluation-harness: MIT).
- Where can I find alternatives to instruct-eval or lm-evaluation-harness?
- GraphCanon lists graph-backed alternatives at instruct-eval alternatives and lm-evaluation-harness alternatives (instruct-eval markdown twin, lm-evaluation-harness markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, instruct-eval or lm-evaluation-harness?
- instruct-eval: Dormant. lm-evaluation-harness: Active. 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 instruct-eval and lm-evaluation-harness?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: instruct-eval trust report; lm-evaluation-harness trust report.