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*This model was released on 2016-07-01 and added to Hugging Face Transformers on 2025-10-07.*

# VaultGemma

[VaultGemma](https://services.google.com/fh/files/blogs/vaultgemma_tech_report.pdf) is a 1-billion-parameter model in the Gemma family that has been fully trained with differential privacy, ensuring strong privacy guarantees during training. It was pretrained on the same data mixture used for the Gemma 2 series, maintaining consistency with prior work while adding privacy-preserving capabilities. This marks a notable advancement in developing large language models that balance utility with privacy protection. The model has been openly released to the research community for broader use and evaluation.

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

```py
import torch
from transformers import pipeline

pipeline = pipeline(task="text-generation", model="google/vaultgemma-1b", dtype="auto",)
pipeline("Plants create energy through a process known as photosynthesis.")
```

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

```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

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

inputs = tokenizer("Plants create energy through a process known as photosynthesis.", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))
```

</hfoption>
</hfoptions>

## VaultGemmaConfig

[[autodoc]] VaultGemmaConfig

## VaultGemmaModel

[[autodoc]] VaultGemmaModel
    - forward

## VaultGemmaForCausalLM

[[autodoc]] VaultGemmaForCausalLM
