ModelCloud/GPTQModel
Apr 2025 – Apr 2025
1 Month active
Languages Used
Python
Technical Skills
Deep LearningModel QuantizationPyTorch
cecilwang
PROFILE
Cecilwang
Worked on stabilizing the quantization path for 1D convolutions in the ModelCloud/GPTQModel repository, focusing on improving input data handling during model quantization. Addressed a bug in the reshaped input calculation to ensure that PyTorch’s nn.Unfold correctly applies kernel size, dilation, padding, and stride parameters for 1D convolution layers. This fix enhanced the reliability and accuracy of quantization results, reducing the risk of downstream model performance issues. Demonstrated expertise in deep learning, model quantization, and Python, with a strong emphasis on debugging and code review. The work contributed to a more robust quantization workflow for 1D convolutional models.
Overall Statistics
Feature vs Bugs
0%Features
Repository Contributions
1Total
Bugs
1
Commits
1
Features
0
Lines of code
40
Activity Months1
Your Network
22 peopleShared Repositories
22
Work History
April 2025
1 Commits
Apr 1, 2025Month: 2025-04. This month focused on stabilizing the GPTQModel quantization path for 1D convolutions. The primary bug fix corrected reshaped_inp calculation so that quantization input preparation uses the correct kernel size, dilation, padding, and stride for 1D Conv layers via nn.Unfold. Implemented in commit 31051a5e25c4c36220a7d037d59a1a82206e8ce8 ('Fix input processing for convolution (#1554)'). Impact: increased reliability and accuracy of quantization results for 1D conv paths, reducing risk of incorrect quantization and downstream model performance issues. Technologies/skills demonstrated: PyTorch internals (nn.Unfold), 1D convolution quantization workflow, debugging, code review, version control. Repo: ModelCloud/GPTQModel.
1 Commits
Apr 1, 2025Month: 2025-04. This month focused on stabilizing the GPTQModel quantization path for 1D convolutions. The primary bug fix corrected reshaped_inp calculation so that quantization input preparation uses the correct kernel size, dilation, padding, and stride for 1D Conv layers via nn.Unfold. Implemented in commit 31051a5e25c4c36220a7d037d59a1a82206e8ce8 ('Fix input processing for convolution (#1554)'). Impact: increased reliability and accuracy of quantization results for 1D conv paths, reducing risk of incorrect quantization and downstream model performance issues. Technologies/skills demonstrated: PyTorch internals (nn.Unfold), 1D convolution quantization workflow, debugging, code review, version control. Repo: ModelCloud/GPTQModel.
April 2025
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance60.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Deep LearningModel QuantizationPyTorch
Repositories Contributed To
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