intel/auto-round - Open Source PR Review Scorecard

A SOTA quantization toolkit for high-accuracy low-bit LLM inference, seamlessly optimized for CPU/XPU/CUDA, with multi-datatype support and full compatibility with vLLM, SGLang, and Transformers|简洁且高效的量化工具包

C-Rank Grade: A (Welcoming) - 62/100

External PR Merge Rate: 77%

Response Time: 2d

First Timer Success: 71%

Frequently Asked Questions

Is intel/auto-round welcoming to first-time open-source contributors?

intel/auto-round has a recorded first-timer success rate of 70.8%. Repositories ranked A typically provide actionable feedback during code reviews and actively nurture new community contributors.

How fast can I expect code review feedback on my pull request?

Maintainers in intel/auto-round respond to incoming external pull requests in approximately 59.4 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.

What does the 62.2 C-Rank™ score (A Tier) represent?

The C-Rank™ system evaluates GitHub projects on a 0–100 scale using real data: PR merge rates, review turnaround time, active maintainer presence, and first-time contributor success. A score of 62.2 places intel/auto-round in the A tier.

What is the external contributor pull request merge rate for intel/auto-round?

The external contributor pull request merge rate for intel/auto-round is 77.2%, based on public PR activity from non-core contributors.

Are there Good First Issues available in intel/auto-round?

intel/auto-round currently has 3 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".

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intel/auto-round

1,587
AWelcoming(62/100)Python

A SOTA quantization toolkit for high-accuracy low-bit LLM inference, seamlessly optimized for CPU/XPU/CUDA, with multi-datatype support and full compatibility with vLLM, SGLang, and Transformers|简洁且高效的量化工具包

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AI Maintainer Review Guidelines

Review Persona

Welcoming Community Builder

Warmth Score
9.5/10
Patience Score
9.2/10
Nitpick Rate
25%

Highly welcoming maintainers in intel/auto-round. Prompt code reviews with positive guidance for new contributors.

Top PR Submission Do's

  • Ensure code complies with the project coding style
  • Keep PRs scoped to a single concern
  • Include context and link to the related issue

Top PR Friction Pitfalls (Don'ts)

  • Do not submit PRs without linking an issue
  • Do not break existing tests without fixing them
  • Do not mix unrelated refactors in a single PR
Response Velocity
2 days+
Standard maintainer review cycle

Average Response Latency

Tracks hours until a maintainer leaves a review, comment, or PR response.

Merge Efficiency
77.2%
High acceptance rate for external PRs

External Acceptance Rate

Percentage of community pull requests successfully merged into main.

First-Timer Success
70.8%
Strong first-timer PR acceptance rate

First PR Conversion

Rate at which developers submitting their first repository PR succeed.

Active Maintainers
19 core
Highly collaborative maintainer core
Diagnostic Health HUD
77.2%
Merge Gauge
70.8%
1st-Timer
Community Vibe52/100

Embed C-Rank Badge

Show contributors that your repository actively reviews and merges external pull requests.

GetMerged C-Rank badge for intel/auto-round
[![GetMerged C-Rank](https://getmerged.abhishekco.de/api/badge/intel/auto-round)](https://getmerged.abhishekco.de/intel/auto-round?utm_source=github&utm_medium=badge)

Active Good First Issues (3)

View on GitHub

This repository currently has 3 open good first issue tickets available for newcomers.

View all good first issues directly on GitHub

Looking for more Python beginner tasks?Explore Python GFI

Contributor Community Vibe Feedback

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Rate your first-hand PR experience (review speed, maintainer responsiveness, and onboarding ease) to help other contributors.

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Contributor Compatibility & Review Speed Analysis for intel/auto-round

When evaluating whether to contribute to intel/auto-round, response velocity and maintainer engagement are crucial. GetMerged continuously tracks pull request trajectories, first-comment latency, and code review rounds to help developers avoid submitting pull requests to backlogged repositories.

Currently, maintainers of intel/auto-round acknowledge new external contributions in approximately 2 days+. Out of all submitted pull requests from non-core authors in the last 180-day window, 77.2% were successfully merged into the primary branch.

Frequently Asked Questions - Contributing to intel/auto-round

01

Is intel/auto-round welcoming to first-time open-source contributors?

intel/auto-round has a recorded first-timer success rate of 70.8%. Repositories ranked Welcoming typically provide actionable feedback during code reviews and actively nurture new community contributors.

02

How fast can I expect code review feedback on my pull request?

The initial maintainer response time averages ~2 days+. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.

03

What does the 62.2 C-Rank™ score represent?

The C-Rank™ index scores repositories on a 0 to 100 scale using an objective formula: external PR merge rates, initial response speed, active maintainer count, and first-time contributor retention. A score of 62.2 places intel/auto-round in the Welcoming tier.

04

What is the external contributor pull request merge rate for intel/auto-round?

The external pull request merge rate is 77.2%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.

05

Are there beginner Good First Issues open in intel/auto-round?

Yes, intel/auto-round currently has 3 active issue(s) tagged with beginner-friendly labels. You can inspect these directly from the repository issues tab.

GetMerged C-Rank™ Indexing Standard

All metrics displayed for intel/auto-round are automatically retrieved via the public GitHub API and recalculated daily. Insider pull requests submitted by repository owners or organization members are excluded from merge rate calculations to preserve objective external contributor statistics.