How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Unispac/Gemma-2-9B-IT-With-Deeper-Safety-Alignment")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Unispac/Gemma-2-9B-IT-With-Deeper-Safety-Alignment")
model = AutoModelForCausalLM.from_pretrained("Unispac/Gemma-2-9B-IT-With-Deeper-Safety-Alignment", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
Quick Links

A Gemma-2-9B-IT checkpoint implemented with deeper safety alignment, using the data augmentation approach proposed in the paper Safety Alignment Should Be Made More Than Just a Few Tokens Deep

Citation:

@article{qi2024safety,
  title={Safety Alignment Should Be Made More Than Just a Few Tokens Deep},
  author={Qi, Xiangyu and Panda, Ashwinee and Lyu, Kaifeng and Ma, Xiao and Roy, Subhrajit and Beirami, Ahmad and Mittal, Prateek and Henderson, Peter},
  journal={arXiv preprint arXiv:2406.05946},
  year={2024}
}
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