To run this, press "Runtime" and press "Run all" on a free Tesla T4 Google Colab instance!
To install Unsloth on your local device, follow our guide. This notebook is licensed LGPL-3.0.
You will learn how to do data prep, how to train, how to run the model, & how to save it
News
Train MoEs - DeepSeek, GLM, Qwen and gpt-oss 12x faster with 35% less VRAM. Blog
You can now train embedding models 1.8-3.3x faster with 20% less VRAM. Blog
Ultra Long-Context Reinforcement Learning is here with 7x more context windows! Blog
3x faster LLM training with 30% less VRAM and 500K context. 3x faster • 500K Context
New in Reinforcement Learning: FP8 RL • Vision RL • Standby • gpt-oss RL
Visit our docs for all our model uploads and notebooks.
Installation
Unsloth
FastModel supports loading nearly any model now! This includes Vision and Text models!
Thank you to Etherl for creating this notebook!
🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning. 🦥 Unsloth Zoo will now patch everything to make training faster! ==((====))== Unsloth 2025.3.19: Fast Llama patching. Transformers: 4.48.0. \\ /| Tesla T4. Num GPUs = 1. Max memory: 14.741 GB. Platform: Linux. O^O/ \_/ \ Torch: 2.6.0+cu124. CUDA: 7.5. CUDA Toolkit: 12.4. Triton: 3.2.0 \ / Bfloat16 = FALSE. FA [Xformers = 0.0.29.post3. FA2 = False] "-____-" Free license: http://github.com/unslothai/unsloth Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!
Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]
unsloth/Llasa-3B does not have a padding token! Will use pad_token = <|finetune_right_pad_id|>.
We now add LoRA adapters so we only need to update 1 to 10% of all parameters!
Not an error, but Unsloth cannot patch MLP layers with our manual autograd engine since either LoRA adapters are not enabled or a bias term (like in Qwen) is used. Not an error, but Unsloth cannot patch Attention layers with our manual autograd engine since either LoRA adapters are not enabled or a bias term (like in Qwen) is used. Not an error, but Unsloth cannot patch O projection layer with our manual autograd engine since either LoRA adapters are not enabled or a bias term (like in Qwen) is used. Unsloth 2025.3.19 patched 28 layers with 0 QKV layers, 0 O layers and 0 MLP layers.
You are using a model of type xcodec2 to instantiate a model of type xcodec. This is not supported for all configurations of models and can yield errors. Processing: 100%|██████████| 1195/1195 [09:41<00:00, 2.05it/s]
Dataset loaded for split 'train'. Number of samples: 1195 Moving XCodec2 model to cpu
Train the model
Now let's use Hugging Face Trainer! More docs here: Transformers docs. We do 60 steps to speed things up, but you can set num_train_epochs=1 for a full run, and turn off max_steps=None.
GPU = Tesla T4. Max memory = 14.741 GB. 5.713 GB of memory reserved.
==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1 \\ /| Num examples = 1,195 | Num Epochs = 1 | Total steps = 149 O^O/ \_/ \ Batch size per device = 2 | Gradient accumulation steps = 4 \ / Data Parallel GPUs = 1 | Total batch size (2 x 4 x 1) = 8 "-____-" Trainable parameters = 36,700,160/3,450,801,152 (1.06% trained)
Unsloth: Will smartly offload gradients to save VRAM!
('lora_model/tokenizer_config.json',
, 'lora_model/special_tokens_map.json',
, 'lora_model/tokenizer.json') Saving to float16
We also support saving to float16 directly. Select merged_16bit for float16 or merged_4bit for int4. We also allow lora adapters as a fallback. Use push_to_hub_merged to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens. See our docs for more deployment options.
Unsloth: You have 1 CPUs. Using `safe_serialization` is 10x slower. We shall switch to Pytorch saving, which might take 3 minutes and not 30 minutes. To force `safe_serialization`, set it to `None` instead. Unsloth: Kaggle/Colab has limited disk space. We need to delete the downloaded model which will save 4-16GB of disk space, allowing you to save on Kaggle/Colab. Unsloth: Will remove a cached repo with size 15.1G
Unsloth: Merging 4bit and LoRA weights to 16bit... Unsloth: Will use up to 3.99 out of 12.67 RAM for saving. Unsloth: Saving model... This might take 5 minutes ...
100%|██████████| 28/28 [00:01<00:00, 27.83it/s]
Unsloth: Saving tokenizer... Done. Unsloth: Saving model/pytorch_model-00001-of-00002.bin... Unsloth: Saving model/pytorch_model-00002-of-00002.bin... Done.
And we're done! If you have any questions on Unsloth, we have a Discord channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!
Some other resources:
- Looking to use Unsloth locally? Read our Installation Guide for details on installing Unsloth on Windows, Docker, AMD, Intel GPUs.
- Learn how to do Reinforcement Learning with our RL Guide and notebooks.
- Read our guides and notebooks for Text-to-speech (TTS) and vision model support.
- Explore our LLM Tutorials Directory to find dedicated guides for each model.
- Need help with Inference? Read our Inference & Deployment page for details on using vLLM, llama.cpp, Ollama etc.
