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Fine-tuned Llama 3.1 8B Instruct model for safety alignment using Weight space Rotation Process (WaRP).
Model Details
Base Model
: meta-llama/Llama-3.1-8B-Instruct
Training Method
: Safety-First WaRP (3-Phase pipeline)
Training Date
: 2026-04-28
Training Procedure
Phase 1: Basis Construction
Collected activations from FFN layers using safety data
Computed SVD to obtain orthonormal basis vectors
Identified 419 important neurons in layer 31
Phase 2: Importance Scoring
Calculated importance scores using gradient-based methods
Generated masks for important directions
Used teacher forcing on safety responses
Phase 3: Incremental Learning
Fine-tuned on utility task (GSM8K) with gradient masking
Protected important directions to maintain safety
Improved utility while preserving safety mechanisms
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "kmseong/WaRP-Safety-Llama3_8B_Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
# Generate text
inputs = tokenizer("What is machine learning?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0]))
Safety Features
✅ Protected safety mechanisms through gradient masking
✅ Maintained refusal capability for harmful requests
✅ Improved utility on reasoning tasks
✅ Balanced safety-utility tradeoff
Datasets
Safety Data
: LibrAI/do-not-answer
Utility Data
: openai/gsm8k
Citation
@article{warp-safety,
title={Safety-First WaRP: Weight space Rotation Process for LLM Safety Alignment},
author={Min-Seong Kim},
year={2026}
}
License
This model is built on Llama 3.1 8B Instruct and follows the same license.
Disclaimer
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.
Runs of kmseong llama3_2_3b-instruct-SSFT-lr5e-5 on huggingface.co
425
Total runs
0
24-hour runs
0
3-day runs
22
7-day runs
425
30-day runs
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