GLM-4.6-HS-LoRA-CurriculumLearning

A LoRA fine-tuned version of GLM-4.6 (356B MoE) trained on the Hyperswitch codebase using Phased Curriculum Learning.

Model Description

This model is specifically trained to understand and assist with the Hyperswitch payment orchestration codebase. It was trained using a 3-phase curriculum learning approach on multi-node H200 GPUs with PyTorch FSDP.

Key Features

• 🎯 Domain-Specific: Trained exclusively on Hyperswitch Rust codebase
• 📚 Curriculum Learning: 3-phase progressive training (Foundation → Evolution → PR Mastery)

Training Details

Hardware Configuration

Component	Specification
GPUs	16× NVIDIA H200 (144GB each)
Nodes	2 nodes × 8 GPUs
Distributed Strategy	PyTorch FSDP (Full Shard)
Precision	BF16 Mixed Precision

LoRA Configuration

Parameter	Value
LoRA Rank (r)	64
LoRA Alpha	128
LoRA Dropout	0.05
Target Modules	q_proj, k_proj, v_proj, o_proj
Trainable Parameters	736 tensors

Training Hyperparameters

Parameter	Value
Effective Batch Size	32 (1 × 2 grad_accum × 16 GPUs)
Sequence Length	16,384 tokens
Chunk Overlap	2,048 tokens
LR Scheduler	Cosine
Weight Decay	0.01
Max Grad Norm	1.0
Precision	BF16

Curriculum Learning Phases

The model was trained using a 3-phase curriculum learning approach, where each phase builds upon the previous:

Phase 1: Foundation (2 epochs)

Metric	Value
Dataset	Codebase structure and file patterns
Samples	9,293 train / 512 eval
Learning Rate	2.5e-5
Warmup Ratio	0.15
Training Time	32.3 hours
Final Eval Loss	0.349
Final Eval Accuracy	90.6%

Phase 2: Evolution (2 epochs)

Metric	Value
Dataset	Commit patterns and code changes
Samples	16,622 train / 1,545 eval
Learning Rate	2.0e-5
Warmup Ratio	0.10
Training Time	64.5 hours
Final Eval Loss	2.46
Final Eval Accuracy	42.3%

Note: Higher loss in Phase 2 is expected due to the complexity of diff/commit patterns.

Phase 3: PR Mastery (1 epoch)

Metric	Value
Dataset	Pull request and review patterns
Samples	9,797 train / 509 eval
Learning Rate	1.5e-5
Warmup Ratio	0.05
Training Time	17.8 hours
Final Eval Loss	0.472
Final Eval Accuracy	90.8%

Training Summary

Metric	Value
Total Training Time	116.5 hours
Total Steps	1,926
Total Epochs	5 (2 + 2 + 1)
Initial Train Loss	0.609
Final Train Loss	0.465
Final Perplexity	1.60

Citation

If you use this model, please cite:

@misc{glm46-hs-lora-curriculum,
  title = {GLM-4.6-HS-LoRA-CurriculumLearning},
  author = {Aditya Narayan},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/AdityaNarayan/GLM-4.6-HS-LoRA-CurriculumLearning}
}

Acknowledgments

• Base model: GLM-4.6 by Zhipu AI
• Training framework: PyTorch FSDP + PEFT
• Dataset: Hyperswitch open-source repository by Juspay