A unified, high-performance framework for training LLMs, VLMs, diffusion, and embodied models on NVIDIA GPUs and Kunlun XPUs.
C-Rank Grade: S (Elite) - 71/100
External PR Merge Rate: 89%
Response Time: 3d
First Timer Success: 87%
baidu-baige/LoongForge has a recorded first-timer success rate of 86.7%. Repositories ranked S typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in baidu-baige/LoongForge respond to incoming external pull requests in approximately 64.9 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.
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 70.8 places baidu-baige/LoongForge in the S tier.
The external contributor pull request merge rate for baidu-baige/LoongForge is 88.9%, based on public PR activity from non-core contributors.
baidu-baige/LoongForge currently has 1 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".
Highly welcoming maintainers in baidu-baige/LoongForge. Prompt code reviews with positive guidance for new contributors.
Average Response Latency
Tracks hours until a maintainer leaves a review, comment, or PR response.
External Acceptance Rate
Percentage of community pull requests successfully merged into main.
First PR Conversion
Rate at which developers submitting their first repository PR succeed.
Show contributors that your repository actively reviews and merges external pull requests.
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Historical health & responsiveness evolution
External PR acceptance
First PR success rate
50% receive response in
90% receive response in
Maintainer review activity heatmap & prime window prediction
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Detected: UTC
Maintainers are most responsive around 11:00 UTC (11:00 UTC in your timezone). Recommended submission window is Monday - Thursday from 09:00 to 13:00 UTC (09:00 - 13:00 UTC local).
Limited recent maintainer review activity recorded for this repository. Prediction based on heuristic baseline.
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~42 hours (96% approval). Large PRs (>500 lines) take ~311 hours (40% approval). Keep your diff compact for rapid review.
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Summary Add INT4 Quantization-Aware Training (QAT) support for MoE expert linear layers, enabling INT4 fake quantization/dequantization during training to reduce the accuracy loss when deploying with INT4 inference. Note: This issue is specifically scoped to INT4 QAT only. Other quantization bit-widths (e.g., INT8, FP8) are out of scope and should be tracked separately if needed. Motivation MoE models have a large number of expert linear layers (TEGroupedLinear), which dominate memory and compute. INT4 quantization can significantly reduce the model footprint and improve inference throughput, but post-training quantization (PTQ) often leads to unacceptable accuracy degradation for these sensitive layers. QAT mitigates this by simulating INT4 quantization effects during training, allowing the model to adapt to the lower precision. Proposed Feature INT4 QAT CUDA kernels: Fake quantization and dequantization kernels that simulate INT4 rounding behavior during the forward pass while kee
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When evaluating whether to contribute to baidu-baige/LoongForge, 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 baidu-baige/LoongForge acknowledge new external contributions in approximately 2 days+. Out of all submitted pull requests from non-core authors in the last 180-day window, 88.9% were successfully merged into the primary branch.
baidu-baige/LoongForge has a recorded first-timer success rate of 86.7%. Repositories ranked Elite typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~2 days+. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.
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 70.8 places baidu-baige/LoongForge in the Elite tier.
The external pull request merge rate is 88.9%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
Yes, baidu-baige/LoongForge currently has 1 active issue(s) tagged with beginner-friendly labels. You can inspect these directly from the repository issues tab.
All metrics displayed for baidu-baige/LoongForge 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.