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

# VaultGemma

## Overview

[VaultGemma](https://services.google.com/fh/files/blogs/vaultgemma_tech_report.pdf) is a text-only decoder model
derived from [Gemma 2](https://huggingface.co/docs/transformers/en/model_doc/gemma2), 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](https://huggingface.co/collections/google/gemma-2-release-667d6600fd5220e7b967f315),
consisting of a number of documents of varying lengths. Additionally, it is trained using
[DP stochastic gradient descent (DP-SGD)](https://huggingface.co/papers/1607.00133) 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.

<hfoptions id="usage">
<hfoption id="Pipeline">

```python
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)
```

</hfoption>
<hfoption id="AutoModel">

```python
# 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]))
```

</hfoption>
</hfoptions>

## VaultGemmaConfig

[[autodoc]] VaultGemmaConfig

## VaultGemmaModel

[[autodoc]] VaultGemmaModel
    - forward

## VaultGemmaForCausalLM

[[autodoc]] VaultGemmaForCausalLM
