Instructions to use sidchalamala/aether-coder-1.5b-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use sidchalamala/aether-coder-1.5b-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-1.5B-Instruct") model = PeftModel.from_pretrained(base_model, "sidchalamala/aether-coder-1.5b-lora") - Notebooks
- Google Colab
- Kaggle
Model card: round held-out v1 intervals to 2 decimals (numbers unchanged)
Browse files
README.md
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@@ -19,9 +19,9 @@ Each task asks for something the policy forbids or Aether cannot do. The correct
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| System | Correct | 95% CI |
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| Base model + reference card + 3 examples | 0/30 | 0.
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| v0.1 (synthetic data only) | 6/30 | 0.
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| **v0.2 (this adapter: + 413 trap samples)** | **6/30** | 0.
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- **Negative result:** adding 413 targeted trap samples did **not** improve held-out performance. v0.2 vs v0.1: 6 vs 6, paired exact McNemar p = 1.
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- v0.2 vs the base model: p = 0.031.
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| System | Correct | 95% CI |
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| Base model + reference card + 3 examples | 0/30 | 0.00-0.11 |
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| v0.1 (synthetic data only) | 6/30 | 0.10-0.37 |
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| **v0.2 (this adapter: + 413 trap samples)** | **6/30** | 0.10-0.37 |
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- **Negative result:** adding 413 targeted trap samples did **not** improve held-out performance. v0.2 vs v0.1: 6 vs 6, paired exact McNemar p = 1.
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- v0.2 vs the base model: p = 0.031.
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