84 lines
2.1 KiB
Markdown
84 lines
2.1 KiB
Markdown
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---
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license: llama3.1
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language:
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- en
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library_name: transformers
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tags:
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- llama
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- safety
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- alignment
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- warp
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---
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# WaRP-Safety-Llama3_8B_Instruct
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Fine-tuned Llama 3.1 8B Instruct model for safety alignment using Weight space Rotation Process (WaRP).
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## Model Details
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- **Base Model**: meta-llama/Llama-3.1-8B-Instruct
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- **Training Method**: Safety-First WaRP (3-Phase pipeline)
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- **Training Date**: 2026-04-30
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## Training Procedure
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### Phase 1: Basis Construction
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- Collected activations from FFN layers using safety data
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- Computed SVD to obtain orthonormal basis vectors
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- Identified 419 important neurons in layer 31
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### Phase 2: Importance Scoring
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- Calculated importance scores using gradient-based methods
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- Generated masks for important directions
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- Used teacher forcing on safety responses
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### Phase 3: Incremental Learning
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- Fine-tuned on utility task (GSM8K) with gradient masking
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- Protected important directions to maintain safety
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- Improved utility while preserving safety mechanisms
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "kmseong/WaRP-Safety-Llama3_8B_Instruct"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
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# Generate text
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inputs = tokenizer("What is machine learning?", return_tensors="pt")
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outputs = model.generate(**inputs, max_length=100)
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print(tokenizer.decode(outputs[0]))
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```
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## Safety Features
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- ✅ Protected safety mechanisms through gradient masking
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- ✅ Maintained refusal capability for harmful requests
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- ✅ Improved utility on reasoning tasks
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- ✅ Balanced safety-utility tradeoff
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## Datasets
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- **Safety Data**: LibrAI/do-not-answer
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- **Utility Data**: openai/gsm8k
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## Citation
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```
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@article{warp-safety,
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title={Safety-First WaRP: Weight space Rotation Process for LLM Safety Alignment},
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author={Min-Seong Kim},
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year={2026}
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}
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```
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## License
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This model is built on Llama 3.1 8B Instruct and follows the same license.
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## Disclaimer
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This model is fine-tuned for improved safety. Users should evaluate model outputs for their specific use cases and apply additional safety measures as needed.
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