Gemma 3 4b It Qat GGUF

🚀 Fine-tune Gemma 3 with Unsloth!

This project focuses on fine-tuning Gemma 3 using Unsloth. It provides free Colab notebooks for fine-tuning Gemma 3 (12B), along with detailed model information, training data details, implementation details, and evaluation results.

🚀 Quick Start

• Fine-tune Gemma 3 (12B) for free using our Google Colab notebook here!
• Read our Blog about Gemma 3 support: unsloth.ai/blog/gemma3
• View the rest of our notebooks in our docs here.
• Export your fine-tuned model to GGUF, Ollama, llama.cpp or 🤗HF.

Links

• See our collection for all versions of Gemma 3 including GGUF, 4-bit & 16-bit formats.
• Read our Guide to see how to Run Gemma 3 correctly.
• Unsloth GitHub
• Unsloth Discord
• Documentation

Comparison Table

✨ Features

• Fine-tune Gemma 3 (12B) for free using Google Colab.
• Support for exporting fine-tuned models to multiple formats.
• Comprehensive evaluation results across various benchmarks.

📚 Documentation

Gemma 3 model card

• Model Page: Gemma
• Resources and Technical Documentation:
  • [Gemma 3 Technical Report][g3-tech-report]
  • [Responsible Generative AI Toolkit][rai-toolkit]
  • [Gemma on Kaggle][kaggle-gemma]
  • [Gemma on Vertex Model Garden][vertex-mg-gemma3]
• Terms of Use: [Terms][terms]
• Authors: Google DeepMind

Model Information

Description

Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone.

Inputs and outputs

• Input:
  • Text string, such as a question, a prompt, or a document to be summarized
  • Images, normalized to 896 x 896 resolution and encoded to 256 tokens each
  • Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and 32K tokens for the 1B size
• Output:
  • Generated text in response to the input, such as an answer to a question, analysis of image content, or a summary of a document
  • Total output context of 8192 tokens

Citation

@article{gemma_2025,
    title={Gemma 3},
    url={https://goo.gle/Gemma3Report},
    publisher={Kaggle},
    author={Gemma Team},
    year={2025}
}

Model Data

Training Dataset

These models were trained on a dataset of text data that includes a wide variety of sources. The 27B model was trained with 14 trillion tokens, the 12B model was trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and 1B with 2 trillion tokens. Here are the key components:

• Web Documents: A diverse collection of web text ensures the model is exposed to a broad range of linguistic styles, topics, and vocabulary. The training dataset includes content in over 140 languages.
• Code: Exposing the model to code helps it to learn the syntax and patterns of programming languages, which improves its ability to generate code and understand code-related questions.
• Mathematics: Training on mathematical text helps the model learn logical reasoning, symbolic representation, and to address mathematical queries.
• Images: A wide range of images enables the model to perform image analysis and visual data extraction tasks.

The combination of these diverse data sources is crucial for training a powerful multimodal model that can handle a wide variety of different tasks and data formats.

Data Preprocessing

Here are the key data cleaning and filtering methods applied to the training data:

• CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was applied at multiple stages in the data preparation process to ensure the exclusion of harmful and illegal content.
• Sensitive Data Filtering: As part of making Gemma pre-trained models safe and reliable, automated techniques were used to filter out certain personal information and other sensitive data from training sets.
• Additional methods: Filtering based on content quality and safety in line with [our policies][safety-policies].

Implementation Information

Hardware

Gemma was trained using [Tensor Processing Unit (TPU)][tpu] hardware (TPUv4p, TPUv5p and TPUv5e). Training vision-language models (VLMS) requires significant computational power. TPUs, designed specifically for matrix operations common in machine learning, offer several advantages in this domain:

• Performance: TPUs are specifically designed to handle the massive computations involved in training VLMs. They can speed up training considerably compared to CPUs.
• Memory: TPUs often come with large amounts of high-bandwidth memory, allowing for the handling of large models and batch sizes during training. This can lead to better model quality.
• Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for handling the growing complexity of large foundation models. You can distribute training across multiple TPU devices for faster and more efficient processing.
• Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective solution for training large models compared to CPU-based infrastructure, especially when considering the time and resources saved due to faster training.
• These advantages are aligned with [Google's commitments to operate sustainably][sustainability].

Software

Training was done using [JAX][jax] and [ML Pathways][ml-pathways].

JAX allows researchers to take advantage of the latest generation of hardware, including TPUs, for faster and more efficient training of large models. ML Pathways is Google's latest effort to build artificially intelligent systems capable of generalizing across multiple tasks. This is specially suitable for foundation models, including large language models like these ones.

Together, JAX and ML Pathways are used as described in the [paper about the Gemini family of models][gemini-2-paper]; "the 'single controller' programming model of Jax and Pathways allows a single Python process to orchestrate the entire training run, dramatically simplifying the development workflow."

Evaluation

Benchmark Results

These models were evaluated against a large collection of different datasets and metrics to cover different aspects of text generation:

Reasoning and factuality

STEM and code

Multilingual

📄 License

The model is under the Gemma license.

[g3-tech-report]: [Link to Gemma 3 Technical Report] [rai-toolkit]: [Link to Responsible Generative AI Toolkit] [kaggle-gemma]: [Link to Gemma on Kaggle] [vertex-mg-gemma3]: [Link to Gemma on Vertex Model Garden] [terms]: [Link to Terms of Use] [safety-policies]: [Link to safety policies] [tpu]: [Link to Tensor Processing Unit (TPU)] [jax]: [Link to JAX] [ml-pathways]: [Link to ML Pathways] [gemini-2-paper]: [Link to paper about the Gemini family of models] [sustainability]: [Link to Google's sustainability commitments] mmlu: [Link to MMLU] agieval: [Link to AGIEval] math: [Link to MATH] gsm8k: [Link to GSM8K] gpqa: [Link to GPQA] mbpp: [Link to MBPP] humaneval: [Link to HumanEval]