Instructions to use abcsk123/Code-Centric-Align with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use abcsk123/Code-Centric-Align with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("models/Qwen2.5-Coder-7B") model = PeftModel.from_pretrained(base_model, "abcsk123/Code-Centric-Align") - Notebooks
- Google Colab
- Kaggle
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* **SFT v3 (released)**: **0.671 (+6.8%)** — achieved through precise loss calculation and data cleaning.
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* **DPO Merged**: < 0.628 — highlighting the extreme sensitivity of code models to preference data quality.
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⚠️ Status & Roadmap
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This project is actively under development. Currently, the DPO alignment exhibits performance regression (Pass@1 < 0.628) due to preference data sensitivity. We are investigating advanced filtering and reward modeling to resolve this. Optimized weights will be uploaded as soon as the alignment bottleneck is cleared.
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* **SFT v3 (released)**: **0.671 (+6.8%)** — achieved through precise loss calculation and data cleaning.
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* **DPO Merged**: < 0.628 — highlighting the extreme sensitivity of code models to preference data quality.
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## ⚠️ Status & Roadmap
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This project is actively under development. Currently, the DPO alignment exhibits performance regression (Pass@1 < 0.628) due to preference data sensitivity. We are investigating advanced filtering and reward modeling to resolve this. Optimized weights will be uploaded as soon as the alignment bottleneck is cleared.
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