huggingface--transformers/docs/source/en/model_doc/vaultgemma.md

This model was published in HF papers on 2016-07-01 and contributed to Hugging Face Transformers on 2025-09-12.

VaultGemma

Overview

VaultGemma is a text-only decoder model derived from Gemma 2, notably it drops the norms after the Attention and MLP blocks, and uses full attention for all layers instead of alternating between full attention and local sliding attention. VaultGemma is available as a pretrained model with 1B parameters that uses a 1024 token sequence length.

VaultGemma was trained from scratch with sequence-level differential privacy (DP). Its training data includes the same mixture as the Gemma 2 models, consisting of a number of documents of varying lengths. Additionally, it is trained using DP stochastic gradient descent (DP-SGD) and provides a (ε ≤ 2.0, δ ≤ 1.1e-10)-sequence-level DP guarantee, where a sequence consists of 1024 consecutive tokens extracted from heterogeneous data sources. Specifically, the privacy unit of the guarantee is for the sequences after sampling and packing of the mixture.

Tip

Click on the VaultGemma models in the right sidebar for more examples of how to apply VaultGemma to different language tasks.

The example below demonstrates how to chat with the model with [Pipeline], the [AutoModel] class, or from the command line.

from transformers import pipeline


pipe = pipeline(
    task="text-generation",
    model="google/vaultgemma-1b",
    device_map="auto",
)

text = "Tell me an unknown interesting biology fact about the brain."
outputs = pipe(text, max_new_tokens=32)
response = outputs[0]["generated_text"]
print(response)

# pip install accelerate
from transformers import AutoModelForCausalLM, AutoTokenizer


model_id = "google/vaultgemma-1b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

text = "Tell me an unknown interesting biology fact about the brain."
input_ids = tokenizer(text, return_tensors="pt").to(model.device)

outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))

VaultGemmaConfig

autodoc VaultGemmaConfig

VaultGemmaModel

autodoc VaultGemmaModel - forward

VaultGemmaForCausalLM

autodoc VaultGemmaForCausalLM