Instructions to use bharathmuppa/angular-coder-30b-a3b-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use bharathmuppa/angular-coder-30b-a3b-lora 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, "bharathmuppa/angular-coder-30b-a3b-lora") - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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@@ -22,6 +22,6 @@ model = PeftModel.from_pretrained(model, "bharathmuppa/angular-coder-30b-a3b-lor
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```
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Rank 16, alpha 16, dropout 0.05, target modules: q/k/v/o projections of every
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layer. Trained
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1.
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merged model card for data and training details.
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```
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Rank 16, alpha 16, dropout 0.05, target modules: q/k/v/o projections of every
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layer. Trained 796 optimizer steps (2.0 epochs), final eval loss
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1.1662. `train_summary.json` in this repo holds the run settings. See the
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merged model card for data and training details.
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