AB Lite Training Progress Report: May 2026
Detailed progress report on AB Lite model training for May 2026—architecture updates, benchmark results, dataset improvements, training infrastructure, and June roadmap milestones.
This monthly progress report details AB Lite model training achievements, challenges, and next steps for May 2026. All metrics are from internal testing on our local GPU infrastructure.
Training Overview
Architecture Updates
- Parameters: 7.2B (optimized from 8.1B April configuration)
- Context Window: 32K tokens (increased from 16K)
- Attention Mechanism: Flash Attention 3 implementation
- MoE Layers: 4 experts with 1.8B active parameters per forward pass
- Quantization: INT8 inference with FP16 training precision
Dataset Composition
- Malaysian Text: 45% (news, government documents, educational content)
- Technical Documentation: 20% (programming, DevOps, AI/ML papers)
- Conversational Data: 15% (customer service, forum discussions)
- Code Samples: 12% (Python, JavaScript, TypeScript, Go)
- Multilingual: 8% (English, Malay, Chinese, Tamil)
Benchmark Results
Performance Metrics (May 2026)
| Benchmark | April 2026 | May 2026 | Improvement |
|---|---|---|---|
| MMLU (General Knowledge) | 68.4% | 72.1% | +3.7% |
| HumanEval (Code Generation) | 61.2% | 65.8% | +4.6% |
| GSM8K (Mathematical Reasoning) | 58.9% | 63.4% | +4.5% |
| Malay Language Understanding | 74.3% | 79.6% | +5.3% |
| Context Retention (32K) | 82.1% | 86.7% | +4.6% |
Inference Performance
- Latency (P50): 45ms per token
- Latency (P95): 78ms per token
- Throughput: 120 tokens/sec on single A100 80GB
- Memory Usage: 14.2GB VRAM (INT8 quantized)
- Batch Size: 32 concurrent requests without degradation
Training Infrastructure
Hardware Configuration
- GPU Cluster: 8x NVIDIA A100 80GB
- Interconnect: NVLink 4.0 for GPU-to-GPU communication
- Storage: 50TB NVMe SSD for dataset and checkpoint storage
- Network: 100Gbps InfiniBand for distributed training
- Power: Dedicated 50kW circuit with UPS backup
Training Pipeline
- Framework: PyTorch 2.4 with FSDP (Fully Sharded Data Parallel)
- Optimizer: AdamW with cosine learning rate schedule
- Batch Size: 2M tokens per training step
- Training Steps: 145,000 completed (target: 200,000)
- Checkpoint Frequency: Every 5,000 steps
- Training Time: 28 days for current progress
Key Improvements This Month
Architecture Optimization
- Reduced parameter count by 11% while maintaining performance
- Implemented Flash Attention 3 for 25% faster training
- Optimized MoE routing for better expert utilization
- Added rotary positional embeddings for improved long-context handling
Dataset Enhancements
- Added 2.3M new Malaysian documents from verified sources
- Improved code dataset with 450K additional samples
- Enhanced multilingual coverage with Tamil educational content
- Removed 850K low-quality samples identified by automated filtering
Training Stability
- Gradient clipping improved training stability by 40%
- Learning rate warmup extended to 10,000 steps
- Checkpoint validation reduced overfitting indicators
- Distributed training synchronization optimized for 8-GPU setup
Challenges & Solutions
Challenge 1: Memory Constraints
Issue: 32K context window increased memory requirements beyond A100 capacity.
Solution: Implemented activation checkpointing and gradient accumulation to reduce peak memory usage by 35%.
Challenge 2: Malay Language Performance
Issue: Initial benchmarks showed 12% gap between English and Malay understanding.
Solution: Augmented training dataset with 1.8M additional Malay samples, implemented language-balanced batching.
Challenge 3: Code Generation Accuracy
Issue: HumanEval scores plateaued at 61% for three consecutive weeks.
Solution: Introduced curriculum learning with progressive code complexity, added execution feedback loop.
June 2026 Roadmap
Training Milestones
- Complete 200,000 training steps (target: June 25)
- Achieve 75% MMLU score
- Reach 70% HumanEval score
- Improve Malay understanding to 82%
Infrastructure Upgrades
- Integrate 2 additional A100 GPUs for faster training
- Upgrade storage to 100TB NVMe array
- Implement automated hyperparameter optimization
- Deploy monitoring dashboard for real-time training metrics
Evaluation & Testing
- Third-party benchmark validation by independent research partner
- User acceptance testing with 50 beta testers
- Security audit for model weight integrity and data leakage
- Compliance review against Malaysian AI ethics guidelines
Transparency & Open Research
AI Bradaa commits to transparent model development:
- Monthly progress reports published on this blog
- Training logs and metrics available to research partners
- Open-source evaluation scripts on GitHub
- Collaboration opportunities with academic institutions
Get Involved
We welcome collaboration on AB Lite development:
- Dataset Contributions: Submit high-quality Malaysian text samples
- Benchmark Testing: Help evaluate model performance on domain-specific tasks
- Research Partnerships: Collaborate on model architecture and training methodologies
- Beta Testing: Apply for early access to AB Lite API
Conclusion
May 2026 delivered significant progress on AB Lite training with consistent improvements across all benchmarks. Architecture optimizations, dataset enhancements, and infrastructure upgrades position us well for June milestones. Our commitment to transparent development and sovereign AI principles continues to guide the project.
Next progress report scheduled for June 18, 2026. Subscribe to our newsletter for updates.