first commit

docs/source/en/model_doc/vaultgemma.md

@@ -0,0 +1,96 @@
+<!--Copyright 2025 the HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be rendered properly in your Markdown viewer.
+
+-->
+*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