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instruct-eval

declare-lab/instruct-eval

Quantitative evaluation for instruction-tuned language models

GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026

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553 stars45 forksLast push Mar 10, 2024 Python Apache-2.0

Decision brief

Key facts about instruct-eval

Good fit when

  • When you need to quantitatively evaluate the performance of instruction-tuned large language models such as Alpaca and Flan-T5 on held-out tasks.
  • If you require a comprehensive toolset for both benchmarking and safety assessment, including specialized benchmarks like IMPACT and Red-Eval.

Avoid when

  • 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.
Requirements:
Min 8 GB RAM; Requires Python environment setup and specific dependencies as outlined in the repository's documentation.

Observed Jul 12, 2026 · Source: enrich:decision_facts

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Maintenance and security

Full trust report
Maintenance
Dormant (910d since push)
As of Sep 6, 2026
Provenance
Not a fork · Organization account
As of Sep 6, 2026
Security (OSV)
83 low (83 low)
As of Jul 11, 2026

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

pip install instruct-eval
PyPI

Similar tools

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Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

A toolset for evaluating the performance of instruction-tuned large language models, including benchmarking and safety assessment.

Capability facts

Languages
python

Source: github.language · Sep 6, 2026

Categories

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

Works with ChatGPTChatGPT

Source: README excerpt (regex_v1, Sep 6, 2026)

-Eval** one could jailbreak/red-team GPT-4 with a 65.1% attack success rate and ChatGPT could be jailbroken 73% of the time as measured on DangerousQA and HarmfulQA be
Source link

Tags

README

:camel: 🍮 📚 InstructEval: Towards Holistic Evaluation of Instruction Tuned Large Language Models Paper Model Leaderboard <p align="center" <img src="https://raw.githubusercontent.com/declare lab/instruct eval/main/docs/logo.png" alt="" width="300" height="300" </p 🔥 If you are...

For agents

This page has a .md twin and JSON over the API.

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