Releases: erfanzar/FJFormer
Releases · erfanzar/FJFormer
v0.0.75
Release 0.0.75
What's New
1. Performance Optimization
- Implemented advanced algorithms to enhance processing speed
- Reduced memory footprint for more efficient resource utilization
- Optimized critical paths to improve overall system responsiveness
2. Enhanced NF4 Data Type
- Improved precision and accuracy of NF4 operations
- Optimized NF4 conversions for better performance
3. Structural Enhancements
- Refactored core components for improved modularity
- Introduced a more intuitive project structure for easier navigation and maintenance
- Updated dependency management for better compatibility and future-proofing
v0.0.70
FJFormer 0.0.70 Release Notes
Major Improvements
Documentation
- Comprehensive overhaul of documentation for better clarity and usability
- Added more examples and use cases to help users get started quickly
- Improved API references for easier navigation
LoRA (Low-Rank Adaptation)
- Fixed critical issues affecting LoRA functionality
- Introduced a new option to selectively save only A, B, and Alpha matrices in LoRA instead of the entire merged model
- This feature significantly reduces storage requirements and improves flexibility in model management
Checkpoint Managers
- Updated with enhanced functionality for more efficient model state handling
- Improved integration with distributed training workflows
Removed Features
- The FJFormer Linen API has been deprecated and removed from this version
New Features
ImplicitArray
- Using
core.ImplicitArray
, a powerful abstraction for handling large arrays without instantiation - Enables lazy evaluation and efficient array operations in JAX
Quantization Support
- Added
Array8Bit
for 8-bit quantization- Reduces model size while maintaining good performance
- Introduced
Array4Bit
for 4-bit quantization (NF4)- Offers extreme model compression for scenarios where size is critical
0.0.40
Summery
- Fixing Splash Attention Kernel
- Improving project structure
- adding new loss function useable and related to MoE Models
What's Changed
- Add compute_weighted_cross_entropy_and_accuracy() by @yhavinga in #1
- Fix Cosine with Warmup Scheduler by @w11wo in #2
New Contributors
Full Changelog: v0.0.22...v0.0.40
v0.0.22
Full Changelog: v0.0.16...v0.0.22
v0.0.16
Full Changelog: https://github.com/erfanzar/FJFormer/commits/v0.0.16