Instructions to use code2lora/code2lora-gru with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use code2lora/code2lora-gru with PEFT:
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- Notebooks
- Google Colab
- Kaggle
Code2LoRA-GRU โ streaming hypernetwork
Final checkpoint of the streaming Code2LoRA-GRU used in the paper. A
1-layer GRU rolls the recurrence over per-commit diff embeddings and emits
a rank-16 LoRA adapter for Qwen/Qwen2.5-Coder-1.5B at O(1) per commit.
Files
| File | Description |
|---|---|
code2lora_gru.pt |
Trained GRU + Code2LoRAHead weights (~2.85 GB, fp32). |
metrics.jsonl |
Per-step training metrics (loss, val EM/EditSim/CodeBLEU). |
Training recipe
- 3 epochs of truncated BPTT (window K=16) on
code2lora/code2lora-data-smartcap(train QnAs) pluscode2lora/code2lora-data-commits(commit metadata + diff embeddings). - AdamW + cosine schedule, max-seq-len 8192, bf16, single H100 80 GB.
Companion model
code2lora/code2lora-direct -- the static-snapshot variant.
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