Instructions to use gnuchev/luminus-coder-30b-sft-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-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-adapter") - Notebooks
- Google Colab
- Kaggle
luminus-coder-30b-sft-adapter
LoRA adapter (r=32, attention projections) for Qwen3-Coder-30B-A3B-Instruct, fine-tuned on code tasks. 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) | 0.302 | 0.391 |
| + this adapter | 0.283 | 0.382 |
Generation-1 result: no significant change at this scale; see the linked results repo for the full experiment history. Adapter provided as-is for reproducibility and comparison.
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Model tree for gnuchev/luminus-coder-30b-sft-adapter
Base model
Qwen/Qwen3-Coder-30B-A3B-Instruct