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Gemma 3 readme #2019

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Expand Up @@ -10,54 +10,54 @@
<a href="https://discord.com/invite/unsloth"><img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord button.png" height="48"></a>
<a href="https://docs.unsloth.ai"><img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/Documentation%20Button.png" height="48"></a>

### Finetune Llama 3.3, Mistral, Phi-4, Qwen 2.5 & Gemma 2x faster with 80% less memory!
### Finetune Llama 3.3, Gemma 3, Phi-4, Qwen 2.5 & Mistral 2x faster with 80% less VRAM!

![](https://i.ibb.co/sJ7RhGG/image-41.png)

</div>

## ✨ Finetune for Free

All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, Ollama, vLLM or uploaded to Hugging Face.
Notebooks are beginner friendly. Read our [guide](https://docs.unsloth.ai/get-started/fine-tuning-guide). Add your dataset, click "Run All", and export your finetuned model to GGUF, Ollama, vLLM or Hugging Face.

| Unsloth supports | Free Notebooks | Performance | Memory use |
|-----------|---------|--------|----------|
| **Llama 3.2 (3B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb) | 2x faster | 70% less |
| **GRPO (R1 reasoning)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb) | 2x faster | 80% less |
| **Gemma 3 (4B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma_3_(4B).ipynb) | 1.6x faster | 60% less |
| **Llama 3.2 (3B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb) | 2x faster | 70% less |
| **Phi-4 (14B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb) | 2x faster | 70% less |
| **Llama 3.2 Vision (11B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb) | 2x faster | 50% less |
| **Llama 3.1 (8B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Alpaca.ipynb) | 2x faster | 70% less |
| **Gemma 2 (9B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma2_(9B)-Alpaca.ipynb) | 2x faster | 70% less |
| **Qwen 2.5 (7B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_(7B)-Alpaca.ipynb) | 2x faster | 70% less |
| **Mistral v0.3 (7B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_(7B)-Conversational.ipynb) | 2.2x faster | 75% less |
| **Ollama** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb) | 1.9x faster | 60% less |
| **DPO Zephyr** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Zephyr_(7B)-DPO.ipynb) | 1.9x faster | 50% less |

- See [all our notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks) and [all our models](https://docs.unsloth.ai/get-started/all-our-models)
- **Kaggle Notebooks** for [Llama 3.2 Kaggle notebook](https://www.kaggle.com/danielhanchen/kaggle-llama-3-2-1b-3b-unsloth-notebook), [Llama 3.1 (8B)](https://www.kaggle.com/danielhanchen/kaggle-llama-3-1-8b-unsloth-notebook), [Gemma 2 (9B)](https://www.kaggle.com/code/danielhanchen/kaggle-gemma-7b-unsloth-notebook/), [Mistral (7B)](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook)
- Run notebooks for [Llama 3.2 conversational](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb), [Llama 3.1 conversational](https://colab.research.google.com/drive/15OyFkGoCImV9dSsewU1wa2JuKB4-mDE_?usp=sharing) and [Mistral v0.3 ChatML](https://colab.research.google.com/drive/15F1xyn8497_dUbxZP4zWmPZ3PJx1Oymv?usp=sharing)
- This [continued pretraining notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_(7B)-CPT.ipynb) is for learning another language
- Click [here](https://docs.unsloth.ai/) for detailed documentation for Unsloth.
- **Kaggle Notebooks** for [Llama 3.2 Kaggle notebook](https://www.kaggle.com/danielhanchen/kaggle-llama-3-2-1b-3b-unsloth-notebook), [Llama 3.1 (8B)](https://www.kaggle.com/danielhanchen/kaggle-llama-3-1-8b-unsloth-notebook), [Phi-4 (14B)](https://www.kaggle.com/code/danielhanchen/phi-4-finetuning-unsloth-notebook), [Mistral (7B)](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook)
- See detailed documentation for Unsloth [here](https://docs.unsloth.ai/).

## ⚡ Quickstart

- **Install with pip (recommended)** for Linux devices:
```
pip install unsloth
```
For Windows install instructions, see [here](https://github.com/unslothai/unsloth/edit/main/README.md#windows-installation).
For Windows install instructions, see [here](https://docs.unsloth.ai/get-started/installing-+-updating/windows-installation).

## 🦥 Unsloth.ai News
- 📣 NEW! Introducing Long-context [Reasoning (GRPO)](https://unsloth.ai/blog/grpo) in Unsloth. You can now reproduce DeepSeek-R1's "aha" moment with just 5GB VRAM. Transform Llama, Phi, Mistral etc. into reasoning LLMs!
- 📣 NEW! [DeepSeek-R1](https://unsloth.ai/blog/deepseek-r1) - the most powerful open reasoning models with Llama & Qwen distillations. Run or fine-tune them now! More details: [unsloth.ai/blog/deepseek-r1](https://unsloth.ai/blog/deepseek-r1). All model uploads: [here](https://huggingface.co/collections/unsloth/deepseek-r1-all-versions-678e1c48f5d2fce87892ace5).
- 📣 NEW! [Phi-4](https://unsloth.ai/blog/phi4) by Microsoft is now supported. We also [fixed bugs](https://unsloth.ai/blog/phi4) in Phi-4 and [uploaded GGUFs, 4-bit](https://huggingface.co/collections/unsloth/phi-4-all-versions-677eecf93784e61afe762afa). Try the [Phi-4 Colab notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb)
- 📣 NEW! [**EVERYTHING** is now supported](https://unsloth.ai/blog/gemma3#everything) incuding: full finetuning, pretraining, ALL models (Mixtral, MOE, Cohere, Mamba) and all training algorithms (KTO, DoRA) etc. MultiGPU support coming very soon.
- 📣 NEW! **Gemma 3** by Google: [Read Blog](https://unsloth.ai/blog/gemma3). We [uploaded GGUFs, 4-bit models](https://huggingface.co/collections/unsloth/phi-4-all-versions-677eecf93784e61afe762afa). Try the [Gemma 3 (4B) Colab notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma_3.ipynb)
- 📣 NEW! Introducing Long-context [Reasoning (GRPO)](https://unsloth.ai/blog/grpo) in Unsloth. Train your own reasoning model with just 5GB VRAM. Transform Llama, Phi, Mistral etc. into reasoning LLMs!
- 📣 NEW! [DeepSeek-R1](https://unsloth.ai/blog/deepseek-r1) - the most powerful open reasoning models with Llama & Qwen distillations. Run or fine-tune them now [with our guide](https://unsloth.ai/blog/deepseek-r1). All model uploads: [here](https://huggingface.co/collections/unsloth/deepseek-r1-all-versions-678e1c48f5d2fce87892ace5).
- 📣 NEW! [Phi-4](https://unsloth.ai/blog/phi4) by Microsoft: We also [fixed bugs](https://unsloth.ai/blog/phi4) in Phi-4 and [uploaded GGUFs, 4-bit](https://huggingface.co/collections/unsloth/phi-4-all-versions-677eecf93784e61afe762afa). Try the [Phi-4 Colab notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb)
- 📣 NEW! [Llama 3.3 (70B)](https://huggingface.co/collections/unsloth/llama-33-all-versions-67535d7d994794b9d7cf5e9f), Meta's latest model is supported.
- 📣 NEW! We worked with Apple to add [Cut Cross Entropy](https://arxiv.org/abs/2411.09009). Unsloth now supports 89K context for Meta's Llama 3.3 (70B) on a 80GB GPU - 13x longer than HF+FA2. For Llama 3.1 (8B), Unsloth enables 342K context, surpassing its native 128K support.
- 📣 Introducing Unsloth [Dynamic 4-bit Quantization](https://unsloth.ai/blog/dynamic-4bit)! We dynamically opt not to quantize certain parameters and this greatly increases accuracy while only using <10% more VRAM than BnB 4-bit. See our collection on [Hugging Face here.](https://huggingface.co/collections/unsloth/unsloth-4-bit-dynamic-quants-67503bb873f89e15276c44e7)
- 📣 [Vision models](https://unsloth.ai/blog/vision) now supported! [Llama 3.2 Vision (11B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb), [Qwen 2.5 VL (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2_VL_(7B)-Vision.ipynb) and [Pixtral (12B) 2409](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Pixtral_(12B)-Vision.ipynb)
<details>
<summary>Click for more news</summary>


- 📣 NEW! We worked with Apple to add [Cut Cross Entropy](https://arxiv.org/abs/2411.09009). Unsloth now supports 89K context for Meta's Llama 3.3 (70B) on a 80GB GPU - 13x longer than HF+FA2. For Llama 3.1 (8B), Unsloth enables 342K context, surpassing its native 128K support.
- 📣 We found and helped fix a [gradient accumulation bug](https://unsloth.ai/blog/gradient)! Please update Unsloth and transformers.
- 📣 Try out [Chat interface](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Unsloth_Studio.ipynb)!
- 📣 NEW! Qwen-2.5 including [Coder](https://unsloth.ai/blog/qwen-coder) models are now supported with bugfixes. 14b fits in a Colab GPU! [Qwen 2.5 conversational notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_Coder_(14B)-Conversational.ipynb)
Expand Down Expand Up @@ -103,7 +103,7 @@ See [here](https://github.com/unslothai/unsloth/edit/main/README.md#advanced-pip
You should install the latest version of your GPUs driver. Download drivers here: [NVIDIA GPU Drive](https://www.nvidia.com/Download/index.aspx).

3. **Install Visual Studio C++:**
You will need Visual Studio, with C++ installed. By default, C++ is not installed with [Visual Studio](https://visualstudio.microsoft.com/vs/community/), so make sure you select all of the C++ options. Also select options for Windows 10/11 SDK. For more detailed instructions, see [here](https://docs.unsloth.ai/get-started/installing-+-updating).
You will need Visual Studio, with C++ installed. By default, C++ is not installed with [Visual Studio](https://visualstudio.microsoft.com/vs/community/), so make sure you select all of the C++ options. Also select options for Windows 10/11 SDK. For detailed instructions with options, see [here](https://docs.unsloth.ai/get-started/installing-+-updating).

5. **Install CUDA Toolkit:**
Follow the instructions to install [CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit-archive).
Expand Down