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
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Browse files
README.md
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---
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license: mit
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tags: [code, lora, hypernetwork, peft, recurrent]
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---
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# Code2LoRA-GRU — streaming hypernetwork
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Final checkpoint of the **streaming Code2LoRA-GRU** used in the paper. A
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1-layer GRU rolls the recurrence over per-commit diff embeddings and emits
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a rank-16 LoRA adapter for `Qwen/Qwen2.5-Coder-1.5B` at *O(1)* per commit.
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## Files
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| File | Description |
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|---|---|
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| `code2lora_gru.pt` | Trained GRU + `Code2LoRAHead` weights (~2.85 GB, fp32). |
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| `metrics.jsonl` | Per-step training metrics (loss, val EM/EditSim/CodeBLEU). |
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## Training recipe
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* 3 epochs of truncated BPTT (window K=16) on
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`code2lora/code2lora-data-smartcap` (train QnAs) plus
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`code2lora/code2lora-data-commits` (commit metadata + diff embeddings).
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* AdamW + cosine schedule, max-seq-len 8192, bf16, single H100 80 GB.
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## Companion model
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`code2lora/code2lora-direct` -- the static-snapshot variant.
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