qwen2_5_32b-instruct-gsm8k-rsn-tuned-lr5e-5
This is a Safety Neuron-Tuned (SN-Tune) version of Llama-3.2-3B-Instruct.
Model Description
- Base Model: meta-llama/Llama-3.2-3B-Instruct
- Fine-tuning Method: SN-Tune (Safety Neuron Tuning)
- Training Data: Circuit Breakers dataset (safety alignment data)
- Upload Date: 2026-06-29 03:20:27
What is SN-Tune?
SN-Tune is a selective fine-tuning approach that:
- Detects safety neurons - a small set of neurons critical for safety
- Freezes all non-safety parameters
- Fine-tunes only safety neurons on safety data
This approach allows for:
- Enhanced safety alignment
- Minimal impact on general capabilities
- Parameter-efficient fine-tuning
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "kmseong/qwen2_5_32b-instruct-gsm8k-rsn-tuned-lr5e-5"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Generate text
prompt = "How can I help you today?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0]))
Safety Note
This model has been fine-tuned specifically for safety using the SN-Tune method. It should provide improved safety alignment compared to the base model.
License
This model is licensed under the Apache 2.0 License. See the base model (meta-llama/Llama-3.2-3B-Instruct) for more details.
References
- Base model: meta-llama/Llama-3.2-3B-Instruct
- Safety neurons detection methodology