Instructions to use gnuchev/luminus-coder-30b-sft-r2-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use gnuchev/luminus-coder-30b-sft-r2-adapter with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-Coder-30B-A3B-Instruct") model = PeftModel.from_pretrained(base_model, "gnuchev/luminus-coder-30b-sft-r2-adapter") - Notebooks
- Google Colab
- Kaggle
luminus-coder-30b-sft-r2-adapter
LoRA adapter (r=32, attention projections) for Qwen3-Coder-30B-A3B-Instruct, fine-tuned on verified code solutions including frontier problems. Load with PeftModel.from_pretrained(base_model, this_repo).
Eval (held-out set of 325 coding problems, k=4, unit-test verified)
| pass@1 | pass@4 | |
|---|---|---|
| base (Qwen3-Coder-30B-A3B-Instruct), plain prompt | 0.302 | 0.391 |
| + this adapter, with its intended prompting | 0.359 (two seeds: 0.359, 0.359) | 0.523 / 0.492 |
On a further held-out set of problems the base model had never solved, the adapter reaches pass@8 0.299 (k=8, T=0.7). Adapter provided as-is for reproducibility and comparison; the full experiment history lives in a private results repo.
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Model tree for gnuchev/luminus-coder-30b-sft-r2-adapter
Base model
Qwen/Qwen3-Coder-30B-A3B-Instruct