REPOGEO REPORT · LITE

huggingface/lighteval

Default branch main · commit 78dbee22 · scanned 6/22/2026, 7:07:13 AM

GitHub: 2,456 stars · 492 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

40 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 warn · 0 fail

Objective metadata checks

AI knows your name

3 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface huggingface/lighteval, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Add a 'Why Lighteval?' or 'Comparison' section to the README

Why:

COPY-PASTE FIX

Add a new section to the README, for example, '## Why Choose Lighteval? (Key Differentiators)' or '## Lighteval vs. Alternatives', detailing its lightweight nature, tight Hugging Face ecosystem integration, and focused scope purely on LLM evaluation.

mediumtopics#2

Expand repository topics with more specific LLM evaluation terms

Why:

CURRENT

evaluation, evaluation-framework, evaluation-metrics, huggingface

COPY-PASTE FIX

evaluation, evaluation-framework, evaluation-metrics, huggingface, llm-evaluation, large-language-models, benchmark, model-evaluation

lowreadme#3

Emphasize 'lightweight' and 'Hugging Face ecosystem integration' in the README's introductory paragraph

Why:

CURRENT

Lighteval** is your *all-in-one toolkit* for evaluating LLMs across multiple backends—whether your model is being **served somewhere** or **already loaded in memory**.

COPY-PASTE FIX

Lighteval** is your *lightweight, all-in-one toolkit* for evaluating LLMs across multiple backends—whether your model is being **served somewhere** or **already loaded in memory**. It offers *tight, native integration with the Hugging Face ecosystem* for seamless workflows.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface huggingface/lighteval

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

MLflow

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

MLflow · recommended 2×
Arize AI · recommended 2×
Weights & Biases · recommended 2×
EleutherAI's LM Evaluation Harness · recommended 1×
HELM · recommended 1×

CATEGORY QUERY
How can I comprehensively evaluate large language models across different deployment environments?
you: not recommended
AI recommended (in order):
1. MLflow
2. Arize AI
3. Weights & Biases
4. EleutherAI's LM Evaluation Harness
5. HELM
6. Prometheus
7. Grafana
8. AWS CloudWatch
9. Azure Monitor
10. Google Cloud Monitoring
11. LangChain
AI recommended 11 alternatives but never named huggingface/lighteval. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What tools provide flexible LLM performance evaluation with custom metrics and detailed result analysis?
you: not recommended
AI recommended (in order):
1. LangChain Evaluation
2. Arize AI
3. Phoenix
4. Galileo AI
5. Galileo Evaluate
6. Weights & Biases
7. W&B Prompts
8. DeepEval
9. MLflow
10. Humanloop
AI recommended 10 alternatives but never named huggingface/lighteval. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
pass
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of huggingface/lighteval?
pass
AI named huggingface/lighteval explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts huggingface/lighteval in production, what risks or prerequisites should they evaluate first?
pass
AI named huggingface/lighteval explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo huggingface/lighteval solve, and who is the primary audience?
pass
AI named huggingface/lighteval explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of huggingface/lighteval. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/huggingface/lighteval.svg)](https://repogeo.com/en/r/huggingface/lighteval)

HTML

<a href="https://repogeo.com/en/r/huggingface/lighteval"><img src="https://repogeo.com/badge/huggingface/lighteval.svg" alt="RepoGEO" /></a>

Pro

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huggingface/lighteval — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite