Home/Compare/athina-evals vs instruct-eval

Comparison

athina-evals vs instruct-eval

Verdict

Pick athina-evals if athina-evals is a Python SDK developed for facilitating the evaluation of outputs from large language models through predefined metrics and frameworks; pick instruct-eval if key facts about instruct-eval.

Markdown twin · athina-evals alternatives · instruct-eval alternatives

GraphCanon updated 2w

athina-evals logo

athina-evals

athina-ai/athina-evals

301pushed Jun 6, 2025
vs
instruct-eval logo

instruct-eval

declare-lab/instruct-eval

552pushed Mar 10, 2024

Trust & integrity

Signalathina-evalsinstruct-eval
Maintenance
Dormant (417d since push)
As of 4w · github_public_v1
Dormant (879d since push)
As of 2w · github_public_v1
Provenance
Not a fork · Organization account
As of 4w · github_public_v1
Not a fork · Organization account
As of 2w · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of 1mo · osv@v1
Published findings
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

athina-evals
Python SDK for evaluating LLM generated responses
instruct-eval
Quantitative evaluation for instruction-tuned language models

Stars

athina-evals
301
instruct-eval
552

Forks

athina-evals
22
instruct-eval
45

Open issues

athina-evals
3
instruct-eval
24

Language

athina-evals
Python
instruct-eval
Python

Adopt for

athina-evals
athina-evals is a Python SDK developed for facilitating the evaluation of outputs from large language models through predefined metrics and frameworks.
instruct-eval
Key facts about instruct-eval

Persona

athina-evals
-
instruct-eval
-

Runtime

athina-evals
-
instruct-eval
-

License

athina-evals
-
instruct-eval
The tool is distributed under Apache-2.0 license

Last pushed

athina-evals
Jun 6, 2025
instruct-eval
Mar 10, 2024

Categories

athina-evals
Evaluation & Observability
instruct-eval
Evaluation & Observability

Trust and health

Days since push

athina-evals
417d
instruct-eval
879d

Open issues (now)

athina-evals
3
instruct-eval
24

OSV dependency advisories

athina-evals
No lockfile (source not queried)
instruct-eval
Published findings

Full report

athina-evals
Trust report
instruct-eval
Trust report

Choose athina-evals if…

  • Tags unique to athina-evals: evaluation-framework, evaluation-metrics, llm-eval, llm-evaluation.
  • When comprehensive evaluation of LLM responses is required, leveraging athina's specific tools and metrics
  • More recently updated (last pushed Jun 6, 2025).

When NOT to use athina-evals

  • If open-source alternatives with transparent customization options are preferred over athina-evals' approach
  • In scenarios where API access requirements limit the ability to perform evaluations offline or in private environments

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, llm, 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 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.

Explore

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: athina-evals 301 · instruct-eval 552 (synced Jul 28, 2026).

Common questions

What is the difference between athina-evals and instruct-eval?
athina-evals: Python SDK for evaluating LLM generated responses. instruct-eval: Quantitative evaluation for instruction-tuned language models. See the comparison table for live GitHub stats and shared categories.
When should I choose athina-evals over instruct-eval?
Choose athina-evals over instruct-eval when Tags unique to athina-evals: evaluation-framework, evaluation-metrics, llm-eval, llm-evaluation; When comprehensive evaluation of LLM responses is required, leveraging athina's specific tools and metrics; More recently updated (last pushed Jun 6, 2025).
When should I choose instruct-eval over athina-evals?
Choose instruct-eval over athina-evals 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, llm, 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 athina-evals?
If open-source alternatives with transparent customization options are preferred over athina-evals' approach In scenarios where API access requirements limit the ability to perform evaluations offline or in private environments
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 athina-evals or instruct-eval more popular on GitHub?
instruct-eval has more GitHub stars (552 vs 301). Stars measure visibility, not whether either tool fits your constraints.
Are athina-evals and instruct-eval open source?
Yes - both are open-source projects on GitHub.
Where can I find alternatives to athina-evals or instruct-eval?
GraphCanon lists graph-backed alternatives at athina-evals alternatives and instruct-eval alternatives (athina-evals markdown twin, instruct-eval 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, athina-evals or instruct-eval?
athina-evals: Dormant. 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 athina-evals and instruct-eval?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: athina-evals trust report; instruct-eval trust report.

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