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---
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base_model: google/gemma-3-1b-it
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language:
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- en
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library_name: transformers
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license: gemma
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tags:
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- unsloth
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- transformers
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- gemma3
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- gemma
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- google
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---
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<div>
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<p style="margin-bottom: 0; margin-top: 0;">
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<strong>See <a href="https://huggingface.co/collections/unsloth/gemma-3-67d12b7e8816ec6efa7e4e5b">our collection</a> for all versions of Gemma 3 including GGUF, 4-bit & 16-bit formats.</strong>
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</p>
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<p style="margin-bottom: 0;">
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<em><a href="https://docs.unsloth.ai/basics/tutorial-how-to-run-gemma-3-effectively">Read our Guide</a> to see how to Run Gemma 3 correctly.</em>
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</p>
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<div style="display: flex; gap: 5px; align-items: center; ">
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<a href="https://github.com/unslothai/unsloth/">
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<img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133">
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</a>
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<a href="https://discord.gg/unsloth">
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<img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173">
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</a>
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<a href="https://docs.unsloth.ai/basics/tutorial-how-to-run-deepseek-r1-on-your-own-local-device">
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<img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">
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</a>
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</div>
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<h1 style="margin-top: 0rem;">✨ Fine-tune Gemma 3 with Unsloth!</h1>
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</div>
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- Fine-tune Gemma 3 (12B) for free using our Google [Colab notebook here](https://docs.unsloth.ai/get-started/unsloth-notebooks)!
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- Read our Blog about Gemma 3 support: [unsloth.ai/blog/gemma3](https://unsloth.ai/blog/gemma3)
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- View the rest of our notebooks in our [docs here](https://docs.unsloth.ai/get-started/unsloth-notebooks).
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- Export your fine-tuned model to GGUF, Ollama, llama.cpp or 🤗HF.
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| Unsloth supports | Free Notebooks | Performance | Memory use |
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|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|
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| **GRPO with Gemma 3 (12B)** | [▶️ Start on Colab](https://docs.unsloth.ai/get-started/unsloth-notebooks) | 2x faster | 80% less |
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| **Llama-3.2 (3B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb) | 2.4x faster | 58% less |
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| **Llama-3.2 (11B vision)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb) | 2x faster | 60% less |
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| **Qwen2.5 (7B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_(7B)-Alpaca.ipynb) | 2x faster | 60% less |
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| **Phi-4 (14B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb) | 2x faster | 50% less |
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| **Mistral (7B)** | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_(7B)-Conversational.ipynb) | 2.2x faster | 62% less |
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<br>
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# Gemma 3 model card
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**Model Page**: [Gemma](https://ai.google.dev/gemma/docs/core)
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**Resources and Technical Documentation**:
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* [Gemma 3 Technical Report][g3-tech-report]
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* [Responsible Generative AI Toolkit][rai-toolkit]
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* [Gemma on Kaggle][kaggle-gemma]
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* [Gemma on Vertex Model Garden][vertex-mg-gemma3]
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**Terms of Use**: [Terms][terms]
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**Authors**: Google DeepMind
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## Model Information
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Summary description and brief definition of inputs and outputs.
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### Description
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Gemma is a family of lightweight, state-of-the-art open models from Google,
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built from the same research and technology used to create the Gemini models.
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Gemma 3 models are multimodal, handling text and image input and generating text
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output, with open weights for both pre-trained variants and instruction-tuned
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variants. Gemma 3 has a large, 128K context window, multilingual support in over
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140 languages, and is available in more sizes than previous versions. Gemma 3
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models are well-suited for a variety of text generation and image understanding
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tasks, including question answering, summarization, and reasoning. Their
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relatively small size makes it possible to deploy them in environments with
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limited resources such as laptops, desktops or your own cloud infrastructure,
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democratizing access to state of the art AI models and helping foster innovation
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for everyone.
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### Inputs and outputs
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- **Input:**
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- Text string, such as a question, a prompt, or a document to be summarized
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- Images, normalized to 896 x 896 resolution and encoded to 256 tokens
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each
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- Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and
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32K tokens for the 1B size
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- **Output:**
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- Generated text in response to the input, such as an answer to a
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question, analysis of image content, or a summary of a document
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- Total output context of 8192 tokens
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### Citation
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```none
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@article{gemma_2025,
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title={Gemma 3},
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url={https://goo.gle/Gemma3Report},
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publisher={Kaggle},
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author={Gemma Team},
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year={2025}
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}
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```
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## Model Data
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Data used for model training and how the data was processed.
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### Training Dataset
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These models were trained on a dataset of text data that includes a wide variety
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of sources. The 27B model was trained with 14 trillion tokens, the 12B model was
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trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and
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1B with 2 trillion tokens. Here are the key components:
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- Web Documents: A diverse collection of web text ensures the model is
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exposed to a broad range of linguistic styles, topics, and vocabulary. The
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training dataset includes content in over 140 languages.
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- Code: Exposing the model to code helps it to learn the syntax and
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patterns of programming languages, which improves its ability to generate
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code and understand code-related questions.
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- Mathematics: Training on mathematical text helps the model learn logical
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reasoning, symbolic representation, and to address mathematical queries.
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- Images: A wide range of images enables the model to perform image
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analysis and visual data extraction tasks.
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The combination of these diverse data sources is crucial for training a powerful
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multimodal model that can handle a wide variety of different tasks and data
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formats.
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### Data Preprocessing
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Here are the key data cleaning and filtering methods applied to the training
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data:
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- CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering
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was applied at multiple stages in the data preparation process to ensure
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the exclusion of harmful and illegal content.
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- Sensitive Data Filtering: As part of making Gemma pre-trained models
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safe and reliable, automated techniques were used to filter out certain
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personal information and other sensitive data from training sets.
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- Additional methods: Filtering based on content quality and safety in
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line with [our policies][safety-policies].
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## Implementation Information
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Details about the model internals.
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### Hardware
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Gemma was trained using [Tensor Processing Unit (TPU)][tpu] hardware (TPUv4p,
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TPUv5p and TPUv5e). Training vision-language models (VLMS) requires significant
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computational power. TPUs, designed specifically for matrix operations common in
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machine learning, offer several advantages in this domain:
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- Performance: TPUs are specifically designed to handle the massive
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computations involved in training VLMs. They can speed up training
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considerably compared to CPUs.
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- Memory: TPUs often come with large amounts of high-bandwidth memory,
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allowing for the handling of large models and batch sizes during training.
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This can lead to better model quality.
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- Scalability: TPU Pods (large clusters of TPUs) provide a scalable
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solution for handling the growing complexity of large foundation models.
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You can distribute training across multiple TPU devices for faster and more
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efficient processing.
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- Cost-effectiveness: In many scenarios, TPUs can provide a more
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cost-effective solution for training large models compared to CPU-based
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infrastructure, especially when considering the time and resources saved
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due to faster training.
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- These advantages are aligned with
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[Google's commitments to operate sustainably][sustainability].
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### Software
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Training was done using [JAX][jax] and [ML Pathways][ml-pathways].
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JAX allows researchers to take advantage of the latest generation of hardware,
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including TPUs, for faster and more efficient training of large models. ML
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Pathways is Google's latest effort to build artificially intelligent systems
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capable of generalizing across multiple tasks. This is specially suitable for
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foundation models, including large language models like these ones.
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Together, JAX and ML Pathways are used as described in the
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[paper about the Gemini family of models][gemini-2-paper]; *"the 'single
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controller' programming model of Jax and Pathways allows a single Python
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process to orchestrate the entire training run, dramatically simplifying the
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development workflow."*
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## Evaluation
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Model evaluation metrics and results.
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### Benchmark Results
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These models were evaluated against a large collection of different datasets and
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metrics to cover different aspects of text generation:
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#### Reasoning and factuality
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| Benchmark | Metric | Gemma 3 PT 1B | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
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| ------------------------------ |----------------|:--------------:|:-------------:|:--------------:|:--------------:|
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| [HellaSwag][hellaswag] | 10-shot | 62.3 | 77.2 | 84.2 | 85.6 |
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| [BoolQ][boolq] | 0-shot | 63.2 | 72.3 | 78.8 | 82.4 |
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| [PIQA][piqa] | 0-shot | 73.8 | 79.6 | 81.8 | 83.3 |
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| [SocialIQA][socialiqa] | 0-shot | 48.9 | 51.9 | 53.4 | 54.9 |
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| [TriviaQA][triviaqa] | 5-shot | 39.8 | 65.8 | 78.2 | 85.5 |
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| [Natural Questions][naturalq] | 5-shot | 9.48 | 20.0 | 31.4 | 36.1 |
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| [ARC-c][arc] | 25-shot | 38.4 | 56.2 | 68.9 | 70.6 |
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| [ARC-e][arc] | 0-shot | 73.0 | 82.4 | 88.3 | 89.0 |
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| [WinoGrande][winogrande] | 5-shot | 58.2 | 64.7 | 74.3 | 78.8 |
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| [BIG-Bench Hard][bbh] | few-shot | 28.4 | 50.9 | 72.6 | 77.7 |
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| [DROP][drop] | 1-shot | 42.4 | 60.1 | 72.2 | 77.2 |
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[hellaswag]: https://arxiv.org/abs/1905.07830
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[boolq]: https://arxiv.org/abs/1905.10044
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[piqa]: https://arxiv.org/abs/1911.11641
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[socialiqa]: https://arxiv.org/abs/1904.09728
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[triviaqa]: https://arxiv.org/abs/1705.03551
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[naturalq]: https://github.com/google-research-datasets/natural-questions
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[arc]: https://arxiv.org/abs/1911.01547
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[winogrande]: https://arxiv.org/abs/1907.10641
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[bbh]: https://paperswithcode.com/dataset/bbh
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[drop]: https://arxiv.org/abs/1903.00161
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#### STEM and code
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| Benchmark | Metric | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
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| ------------------------------ |----------------|:-------------:|:--------------:|:--------------:|
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| [MMLU][mmlu] | 5-shot | 59.6 | 74.5 | 78.6 |
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| [MMLU][mmlu] (Pro COT) | 5-shot | 29.2 | 45.3 | 52.2 |
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| [AGIEval][agieval] | 3-5-shot | 42.1 | 57.4 | 66.2 |
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| [MATH][math] | 4-shot | 24.2 | 43.3 | 50.0 |
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| [GSM8K][gsm8k] | 8-shot | 38.4 | 71.0 | 82.6 |
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| [GPQA][gpqa] | 5-shot | 15.0 | 25.4 | 24.3 |
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| [MBPP][mbpp] | 3-shot | 46.0 | 60.4 | 65.6 |
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| [HumanEval][humaneval] | 0-shot | 36.0 | 45.7 | 48.8 |
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[mmlu]: https://arxiv.org/abs/2009.03300
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[agieval]: https://arxiv.org/abs/2304.06364
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[math]: https://arxiv.org/abs/2103.03874
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[gsm8k]: https://arxiv.org/abs/2110.14168
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[gpqa]: https://arxiv.org/abs/2311.12022
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[mbpp]: https://arxiv.org/abs/2108.07732
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[humaneval]: https://arxiv.org/abs/2107.03374
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#### Multilingual
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| Benchmark | Gemma 3 PT 1B | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
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| ------------------------------------ |:-------------:|:-------------:|:--------------:|:--------------:|
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| [MGSM][mgsm] | 2.04 | 34.7 | 64.3 | 74.3 |
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| [Global-MMLU-Lite][global-mmlu-lite] | 24.9 | 57.0 | 69.4 | 75.7 |
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| [WMT24++][wmt24pp] (ChrF) | 36.7 | 48.4 | 53.9 | 55.7 |
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| [FloRes][flores] | 29.5 | 39.2 | 46.0 | 48.8 |
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| [XQuAD][xquad] (all) | 43.9 | 68.0 | 74.5 | 76.8 |
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| [ECLeKTic][eclektic] | 4.69 | 11.0 | 17.2 | 24.4 |
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| [IndicGenBench][indicgenbench] | 41.4 | 57.2 | 61.7 | 63.4 |
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[mgsm]: https://arxiv.org/abs/2210.03057
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[flores]: https://arxiv.org/abs/2106.03193
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[xquad]: https://arxiv.org/abs/1910.11856v3
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[global-mmlu-lite]: https://huggingface.co/datasets/CohereForAI/Global-MMLU-Lite
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[wmt24pp]: https://arxiv.org/abs/2502.12404v1
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[eclektic]: https://arxiv.org/abs/2502.21228
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[indicgenbench]: https://arxiv.org/abs/2404.16816
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#### Multimodal
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| Benchmark | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
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| ------------------------------ |:-------------:|:--------------:|:--------------:|
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| [COCOcap][coco-cap] | 102 | 111 | 116 |
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| [DocVQA][docvqa] (val) | 72.8 | 82.3 | 85.6 |
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| [InfoVQA][info-vqa] (val) | 44.1 | 54.8 | 59.4 |
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| [MMMU][mmmu] (pt) | 39.2 | 50.3 | 56.1 |
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| [TextVQA][textvqa] (val) | 58.9 | 66.5 | 68.6 |
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| [RealWorldQA][realworldqa] | 45.5 | 52.2 | 53.9 |
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| [ReMI][remi] | 27.3 | 38.5 | 44.8 |
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| [AI2D][ai2d] | 63.2 | 75.2 | 79.0 |
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| [ChartQA][chartqa] | 63.6 | 74.7 | 76.3 |
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| [VQAv2][vqav2] | 63.9 | 71.2 | 72.9 |
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| [BLINK][blinkvqa] | 38.0 | 35.9 | 39.6 |
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| [OKVQA][okvqa] | 51.0 | 58.7 | 60.2 |
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| [TallyQA][tallyqa] | 42.5 | 51.8 | 54.3 |
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| [SpatialSense VQA][ss-vqa] | 50.9 | 60.0 | 59.4 |
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| [CountBenchQA][countbenchqa] | 26.1 | 17.8 | 68.0 |
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[coco-cap]: https://cocodataset.org/#home
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[docvqa]: https://www.docvqa.org/
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[info-vqa]: https://arxiv.org/abs/2104.12756
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[mmmu]: https://arxiv.org/abs/2311.16502
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[textvqa]: https://textvqa.org/
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[realworldqa]: https://paperswithcode.com/dataset/realworldqa
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[remi]: https://arxiv.org/html/2406.09175v1
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[ai2d]: https://allenai.org/data/diagrams
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[chartqa]: https://arxiv.org/abs/2203.10244
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[vqav2]: https://visualqa.org/index.html
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[blinkvqa]: https://arxiv.org/abs/2404.12390
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[okvqa]: https://okvqa.allenai.org/
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[tallyqa]: https://arxiv.org/abs/1810.12440
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[ss-vqa]: https://arxiv.org/abs/1908.02660
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[countbenchqa]: https://github.com/google-research/big_vision/blob/main/big_vision/datasets/countbenchqa/
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## Ethics and Safety
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Ethics and safety evaluation approach and results.
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### Evaluation Approach
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Our evaluation methods include structured evaluations and internal red-teaming
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testing of relevant content policies. Red-teaming was conducted by a number of
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different teams, each with different goals and human evaluation metrics. These
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models were evaluated against a number of different categories relevant to
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ethics and safety, including:
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- **Child Safety**: Evaluation of text-to-text and image to text prompts
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covering child safety policies, including child sexual abuse and
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exploitation.
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- **Content Safety:** Evaluation of text-to-text and image to text prompts
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covering safety policies including, harassment, violence and gore, and hate
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speech.
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- **Representational Harms**: Evaluation of text-to-text and image to text
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prompts covering safety policies including bias, stereotyping, and harmful
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associations or inaccuracies.
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In addition to development level evaluations, we conduct "assurance
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evaluations" which are our 'arms-length' internal evaluations for responsibility
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governance decision making. They are conducted separately from the model
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development team, to inform decision making about release. High level findings
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are fed back to the model team, but prompt sets are held-out to prevent
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overfitting and preserve the results' ability to inform decision making.
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Assurance evaluation results are reported to our Responsibility & Safety Council
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as part of release review.
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### Evaluation Results
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For all areas of safety testing, we saw major improvements in the categories of
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child safety, content safety, and representational harms relative to previous
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Gemma models. All testing was conducted without safety filters to evaluate the
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model capabilities and behaviors. For both text-to-text and image-to-text, and
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across all model sizes, the model produced minimal policy violations, and showed
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significant improvements over previous Gemma models' performance with respect
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to ungrounded inferences. A limitation of our evaluations was they included only
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English language prompts.
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## Usage and Limitations
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These models have certain limitations that users should be aware of.
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### Intended Usage
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Open vision-language models (VLMs) models have a wide range of applications
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across various industries and domains. The following list of potential uses is
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not comprehensive. The purpose of this list is to provide contextual information
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about the possible use-cases that the model creators considered as part of model
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training and development.
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- Content Creation and Communication
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- Text Generation: These models can be used to generate creative text
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formats such as poems, scripts, code, marketing copy, and email drafts.
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- Chatbots and Conversational AI: Power conversational interfaces
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for customer service, virtual assistants, or interactive applications.
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- Text Summarization: Generate concise summaries of a text corpus,
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research papers, or reports.
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- Image Data Extraction: These models can be used to extract,
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interpret, and summarize visual data for text communications.
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- Research and Education
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- Natural Language Processing (NLP) and VLM Research: These
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models can serve as a foundation for researchers to experiment with VLM
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|
and NLP techniques, develop algorithms, and contribute to the
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|
advancement of the field.
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|
- Language Learning Tools: Support interactive language learning
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experiences, aiding in grammar correction or providing writing practice.
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- Knowledge Exploration: Assist researchers in exploring large
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bodies of text by generating summaries or answering questions about
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specific topics.
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### Limitations
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|
|
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|
- Training Data
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- The quality and diversity of the training data significantly
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|
influence the model's capabilities. Biases or gaps in the training data
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|
can lead to limitations in the model's responses.
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|
- The scope of the training dataset determines the subject areas
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|
the model can handle effectively.
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|
- Context and Task Complexity
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|
- Models are better at tasks that can be framed with clear
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|
prompts and instructions. Open-ended or highly complex tasks might be
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|
challenging.
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|
- A model's performance can be influenced by the amount of context
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|
provided (longer context generally leads to better outputs, up to a
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|
certain point).
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- Language Ambiguity and Nuance
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|
- Natural language is inherently complex. Models might struggle
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to grasp subtle nuances, sarcasm, or figurative language.
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- Factual Accuracy
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|
- Models generate responses based on information they learned
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|
from their training datasets, but they are not knowledge bases. They
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|
may generate incorrect or outdated factual statements.
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|
- Common Sense
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|
- Models rely on statistical patterns in language. They might
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|
lack the ability to apply common sense reasoning in certain situations.
|
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|
|
|
|
### Ethical Considerations and Risks
|
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|
The development of vision-language models (VLMs) raises several ethical
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|
concerns. In creating an open model, we have carefully considered the following:
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|
- Bias and Fairness
|
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|
- VLMs trained on large-scale, real-world text and image data can
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|
reflect socio-cultural biases embedded in the training material. These
|
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|
models underwent careful scrutiny, input data pre-processing described
|
|
|
and posterior evaluations reported in this card.
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|
- Misinformation and Misuse
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|
- VLMs can be misused to generate text that is false, misleading,
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|
or harmful.
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|
- Guidelines are provided for responsible use with the model, see the
|
|
|
[Responsible Generative AI Toolkit][rai-toolkit].
|
|
|
- Transparency and Accountability:
|
|
|
- This model card summarizes details on the models' architecture,
|
|
|
capabilities, limitations, and evaluation processes.
|
|
|
- A responsibly developed open model offers the opportunity to
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|
share innovation by making VLM technology accessible to developers and
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|
researchers across the AI ecosystem.
|
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|
|
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|
Risks identified and mitigations:
|
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|
|
|
|
- **Perpetuation of biases**: It's encouraged to perform continuous
|
|
|
monitoring (using evaluation metrics, human review) and the exploration of
|
|
|
de-biasing techniques during model training, fine-tuning, and other use
|
|
|
cases.
|
|
|
- **Generation of harmful content**: Mechanisms and guidelines for content
|
|
|
safety are essential. Developers are encouraged to exercise caution and
|
|
|
implement appropriate content safety safeguards based on their specific
|
|
|
product policies and application use cases.
|
|
|
- **Misuse for malicious purposes**: Technical limitations and developer
|
|
|
and end-user education can help mitigate against malicious applications of
|
|
|
VLMs. Educational resources and reporting mechanisms for users to flag
|
|
|
misuse are provided. Prohibited uses of Gemma models are outlined in the
|
|
|
[Gemma Prohibited Use Policy][prohibited-use].
|
|
|
- **Privacy violations**: Models were trained on data filtered for removal
|
|
|
of certain personal information and other sensitive data. Developers are
|
|
|
encouraged to adhere to privacy regulations with privacy-preserving
|
|
|
techniques.
|
|
|
|
|
|
### Benefits
|
|
|
|
|
|
At the time of release, this family of models provides high-performance open
|
|
|
vision-language model implementations designed from the ground up for
|
|
|
responsible AI development compared to similarly sized models.
|
|
|
|
|
|
Using the benchmark evaluation metrics described in this document, these models
|
|
|
have shown to provide superior performance to other, comparably-sized open model
|
|
|
alternatives.
|
|
|
|
|
|
[g3-tech-report]: https://goo.gle/Gemma3Report
|
|
|
[rai-toolkit]: https://ai.google.dev/responsible
|
|
|
[kaggle-gemma]: https://www.kaggle.com/models/google/gemma-3
|
|
|
[vertex-mg-gemma3]: https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/gemma3
|
|
|
[terms]: https://ai.google.dev/gemma/terms
|
|
|
[safety-policies]: https://ai.google/static/documents/ai-responsibility-update-published-february-2025.pdf
|
|
|
[prohibited-use]: https://ai.google.dev/gemma/prohibited_use_policy
|
|
|
[tpu]: https://cloud.google.com/tpu/docs/intro-to-tpu
|
|
|
[sustainability]: https://sustainability.google/operating-sustainably/
|
|
|
[jax]: https://github.com/jax-ml/jax
|
|
|
[ml-pathways]: https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/
|
|
|
[sustainability]: https://sustainability.google/operating-sustainably/
|
|
|
[gemini-2-paper]: https://arxiv.org/abs/2312.11805 |
