from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "wvnvwn/gemma-2-9b-it-lr5e-5-safeinstr-0.1"
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

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: 2026-04-30 14:11:06

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