---
title: "Gemma"
description: ""
---
[Google's Gemma](https://ai.google.dev/gemma) is a family of open-weight language models built on the same research and technology as Gemini. Gemma models are available in multiple sizes and versions, with improvements in each generation including local sliding window attention (Gemma 2) and interleaved global/local attention (Gemma 3).

<Info>

| | |
|---|---|
| **Task** | Text Generation |
| **Architecture** | `GemmaForCausalLM` / `Gemma2ForCausalLM` / `Gemma3ForCausalLM` |
| **Parameters** | 1B – 27B |
| **HF Org** | [google](https://huggingface.co/google) |

</Info>

## Available Models

- **Gemma 3**: 1B, 4B, 12B, 27B
- **Gemma 2**: 2B, 9B, 27B
- **Gemma (v1)**: 2B, 7B

## Architectures

- `GemmaForCausalLM` — Gemma v1
- `Gemma2ForCausalLM` — Gemma 2
- `Gemma3ForCausalLM` — Gemma 3

## Example HF Models

| Model | HF ID |
|---|---|
| Gemma 1.1 2B IT | [`google/gemma-1.1-2b-it`](https://huggingface.co/google/gemma-1.1-2b-it) |
| Gemma 2B | [`google/gemma-2b`](https://huggingface.co/google/gemma-2b) |
| Gemma 2 9B IT | [`google/gemma-2-9b-it`](https://huggingface.co/google/gemma-2-9b-it) |
| Gemma 2 27B | [`google/gemma-2-27b`](https://huggingface.co/google/gemma-2-27b) |
| Gemma 3 1B IT | [`google/gemma-3-1b-it`](https://huggingface.co/google/gemma-3-1b-it) |
| Gemma 3 4B IT | [`google/gemma-3-4b-it`](https://huggingface.co/google/gemma-3-4b-it) |
| Gemma 3 27B IT | [`google/gemma-3-27b-it`](https://huggingface.co/google/gemma-3-27b-it) |

## Example Recipes

| Recipe | Description |
|---|---|
| [gemma_2_9b_it_squad.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/llm_finetune/gemma/gemma_2_9b_it_squad.yaml) | SFT — Gemma 2 9B IT on SQuAD |
| [gemma_2_9b_it_squad_peft.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/llm_finetune/gemma/gemma_2_9b_it_squad_peft.yaml) | LoRA — Gemma 2 9B IT on SQuAD |
| [gemma_3_270m_squad.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/llm_finetune/gemma/gemma_3_270m_squad.yaml) | SFT — Gemma 3 270M on SQuAD |
| [gemma_3_270m_squad_peft.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/llm_finetune/gemma/gemma_3_270m_squad_peft.yaml) | LoRA — Gemma 3 270M on SQuAD |

## Try with NeMo AutoModel

**1. Install** ([full instructions](/get-started/installation)):

```bash
pip install nemo-automodel
```

**2. Clone the repo** to get the example recipes:

```bash
git clone https://github.com/NVIDIA-NeMo/Automodel.git
cd Automodel
```

**3. Run the recipe** from inside the repo:

```bash
automodel --nproc-per-node=8 examples/llm_finetune/gemma/gemma_2_9b_it_squad.yaml
```

<Accordion title="Run with Docker">
**1. Pull the container** and mount a checkpoint directory:

```bash
docker run --gpus all -it --rm \
  --shm-size=8g \
  -v $(pwd)/checkpoints:/opt/Automodel/checkpoints \
  nvcr.io/nvidia/nemo-automodel:26.02.00
```

**2.** Navigate to the AutoModel directory (where the recipes are):

```bash
cd /opt/Automodel
```

**3. Run the recipe**:

```bash
automodel --nproc-per-node=8 examples/llm_finetune/gemma/gemma_2_9b_it_squad.yaml
```
</Accordion>

See the [Installation Guide](/get-started/installation) and [LLM Fine-Tuning Guide](/recipes-e2e-examples/sft-peft).

## Fine-Tuning

See the [LLM Fine-Tuning Guide](/recipes-e2e-examples/sft-peft) for full SFT and LoRA instructions.

## Hugging Face Model Cards

- [google/gemma-2b](https://huggingface.co/google/gemma-2b)
- [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it)
- [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it)
