ml-explore/mlx-lm
Dec 2025 – Jan 2026
2 Months active
Languages Used
Python
Technical Skills
Pythondeep learningmachine learningMachine LearningPython DevelopmentQuantization Techniques
Inferencer
PROFILE
Inferencer
Worked on the ml-explore/mlx-lm repository over a two-month period, focusing on targeted bug fixes to improve model reliability and deployment stability. Addressed an attention scaling compatibility issue by converting cache offsets to an integer format compatible with mx.arange, ensuring correct input scaling and robust tensor dimension handling for varying sequence lengths. Enhanced the quantization pipeline by requiring a mode parameter in the mixed_quant_predicate_builder, preventing runtime exceptions during quantized inference. These contributions demonstrated proficiency in Python, deep learning, and quantization techniques, resulting in more stable training, inference, and deployment workflows for machine learning models within the repository.
Overall Statistics
Feature vs Bugs
0%Features
Repository Contributions
2Total
Bugs
2
Commits
2
Features
0
Lines of code
27
Activity Months2
Your Network
83 peopleShared Repositories
83
Work History
January 2026
1 Commits
Jan 1, 2026January 2026 monthly summary for the ml-explore/mlx-lm repository focused on robustness of the quantization pipeline. Implemented a targeted fix to prevent a runtime exception in the mixed_quant_predicate_builder by introducing the required mode parameter for the nn.quantize class predicate, aligning with MLX's quantization requirements.
1 Commits
Jan 1, 2026January 2026 monthly summary for the ml-explore/mlx-lm repository focused on robustness of the quantization pipeline. Implemented a targeted fix to prevent a runtime exception in the mixed_quant_predicate_builder by introducing the required mode parameter for the nn.quantize class predicate, aligning with MLX's quantization requirements.
January 2026
December 2025
1 Commits
Dec 1, 2025December 2025 monthly summary for ml-explore/mlx-lm: A focused bug fix improved the reliability of the attention scaling path. By converting the cache offset to an integer format compatible with mx.arange, the fix ensures correct position-based input scaling and more robust tensor dimension handling across varying input lengths. This directly addresses the Attention Scaling Compatibility Bug and aligns with Devstral-2 (#671).
December 2025
1 Commits
Dec 1, 2025December 2025 monthly summary for ml-explore/mlx-lm: A focused bug fix improved the reliability of the attention scaling path. By converting the cache offset to an integer format compatible with mx.arange, the fix ensures correct position-based input scaling and more robust tensor dimension handling across varying input lengths. This directly addresses the Attention Scaling Compatibility Bug and aligns with Devstral-2 (#671).
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage30.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Machine LearningPythonPython DevelopmentQuantization Techniquesdeep learningmachine learning
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