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abacaj
code-eval
Python2023–2023
abacaj / code-eval

code-eval

Run evaluation on LLMs using human-eval benchmark

2Distinct people
4Mentions
0Explicit recommenders
432GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Run evaluation on LLMs using human-eval benchmark

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

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This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 7:56:55 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by week4 dated mentions

Observed mentions: 2023-07-07 08:19:12 UTC to 2023-08-14 17:20:23 UTC. Weeks start Monday, UTC.

012023-07-03 00:00:00 UTC to 2023-07-10 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsJul 32023-07-10 00:00:00 UTC to 2023-07-17 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2023-07-17 00:00:00 UTC to 2023-07-24 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-07-24 00:00:00 UTC to 2023-07-31 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsJul 242023-07-31 00:00:00 UTC to 2023-08-07 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-08-07 00:00:00 UTC to 2023-08-14 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-08-14 00:00:00 UTC to 2023-08-21 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsAug 14

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Jul 32023-07-03 00:00:00 UTC2023-07-10 00:00:00 UTC101
Jul 102023-07-10 00:00:00 UTC2023-07-17 00:00:00 UTC101
Jul 172023-07-17 00:00:00 UTC2023-07-24 00:00:00 UTC000
Jul 242023-07-24 00:00:00 UTC2023-07-31 00:00:00 UTC101
Jul 312023-07-31 00:00:00 UTC2023-08-07 00:00:00 UTC000
Aug 72023-08-07 00:00:00 UTC2023-08-14 00:00:00 UTC000
Aug 142023-08-14 00:00:00 UTC2023-08-21 00:00:00 UTC101

What Hacker News says

1 sources
You're looking for "HumanEval" tests. Not saying this is the best way to test it, but it's the only standard test I know of that code models are compared with and are commonly benchmarked for The current best models you'd want to try that I'm aware of is WizardCoder(15B), Starcoder(15B), and replit's code model(3B). Replit's instruct model is interesting because of it's competitive performance while only being a 3B model so it's the easiest/fastest to use. Perhaps interestingly none of these are based on LLama https://github.com/abacaj/code-eval - This is a large mostly up to date list of benchmarks https://huggingface.co/WizardLM/WizardCoder-15B-V1.0 - has a chart with a mostly up to date comparison EDIT: License-wise I think you might be able to commercially use Replit's model and Starcoder, I don't think you're allowed to use WizardCod
Read the rest
er outside of academic work.
In Ask HN: LLM Leaderboard for Code Generation?

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