# /// script # requires-python = ">=3.10,<3.14" # dependencies = [ # "accelerate", # "bitsandbytes>=0.43.0", # "datasets==4.3.0", # "hf_transfer", # "huggingface_hub>=0.34.0", # "marimo", # "peft", # "protobuf", # "sentencepiece", # "torchao>=0.16.0", # "transformers==4.56.2", # "triton>=3.2.0", # "trl==0.22.2", # "unsloth @ git+https://github.com/unslothai/unsloth", # "unsloth_zoo @ git+https://github.com/unslothai/unsloth-zoo", # ] # # [tool.uv] # no-build-package = [ # "bitsandbytes", # "triton", # "vllm", # "xformers", # ] # /// import marimo __generated_with = "0.23.8" app = marimo.App() @app.cell def _(): import marimo as mo return (mo,) @app.cell(hide_code=True) def _(mo): mo.md(r""" To run this notebook, hit the **▶ Run all** button in the bottom-right corner - or use `Ctrl/Cmd + Shift + R`.

Join Discord if you need help + ⭐ Star us on Github ⭐

To install Unsloth on your local device, follow [our guide](https://unsloth.ai/docs/get-started/install). This notebook is licensed [LGPL-3.0](https://github.com/unslothai/notebooks?tab=LGPL-3.0-1-ov-file#readme). You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & how to save it """) return @app.cell(hide_code=True) def _(mo): mo.md(r""" ### News """) return @app.cell(hide_code=True) def _(mo): mo.md(r""" Introducing **Unsloth Studio** - a new open source, no-code web UI to train and run LLMs. [Blog](https://unsloth.ai/docs/new/studio) • [Notebook](https://github.com/unslothai/unsloth/blob/main/studio/Unsloth_Studio_Colab.ipynb)

_{Train models — no code needed}

_{Run GGUF models on Mac, Windows & Linux}

Train MoEs - DeepSeek, GLM, Qwen and gpt-oss 12x faster with 35% less VRAM. [Blog](https://unsloth.ai/docs/new/faster-moe) Ultra Long-Context Reinforcement Learning is here with 7x more context windows! [Blog](https://unsloth.ai/docs/new/grpo-long-context) New in Reinforcement Learning: [FP8 RL](https://unsloth.ai/docs/new/fp8-reinforcement-learning) • [Vision RL](https://unsloth.ai/docs/new/vision-reinforcement-learning-vlm-rl) • [Standby](https://unsloth.ai/docs/basics/memory-efficient-rl) • [gpt-oss RL](https://unsloth.ai/docs/new/gpt-oss-reinforcement-learning) Visit our docs for all our [model uploads](https://unsloth.ai/docs/get-started/unsloth-model-catalog) and [notebooks](https://unsloth.ai/docs/get-started/unsloth-notebooks). """) return @app.cell(hide_code=True) def _(mo): mo.md(r""" ### Unsloth """) return @app.cell def _(): from unsloth import FastVisionModel # FastLanguageModel for LLMs import torch # 4bit pre quantized models we support for 4x faster downloading + no OOMs. fourbit_models = [ "unsloth/Llama-3.2-11B-Vision-Instruct-bnb-4bit", # Llama 3.2 vision support "unsloth/Llama-3.2-11B-Vision-bnb-4bit", "unsloth/Llama-3.2-90B-Vision-Instruct-bnb-4bit", # Can fit in a 80GB card! "unsloth/Llama-3.2-90B-Vision-bnb-4bit", "unsloth/Pixtral-12B-2409-bnb-4bit", # Pixtral fits in 16GB! "unsloth/Pixtral-12B-Base-2409-bnb-4bit", # Pixtral base model "unsloth/Qwen2-VL-2B-Instruct-bnb-4bit", # Qwen2 VL support "unsloth/Qwen2-VL-7B-Instruct-bnb-4bit", "unsloth/Qwen2-VL-72B-Instruct-bnb-4bit", "unsloth/llava-v1.6-mistral-7b-hf-bnb-4bit", # Any Llava variant works! "unsloth/llava-1.5-7b-hf-bnb-4bit", ] # More models at https://huggingface.co/unsloth model, tokenizer = FastVisionModel.from_pretrained( "unsloth/Llama-3.2-11B-Vision-Instruct", load_in_4bit=True, # Use 4bit to reduce memory use. False for 16bit LoRA. use_gradient_checkpointing="unsloth", # True or "unsloth" for long context ) return FastVisionModel, model, tokenizer, torch @app.cell(hide_code=True) def _(mo): mo.md(r""" We now add LoRA adapters for parameter efficient finetuning - this allows us to only efficiently train 1% of all parameters. **[NEW]** We also support finetuning ONLY the vision part of the model, or ONLY the language part. Or you can select both! You can also select to finetune the attention or the MLP layers! """) return @app.cell def _(FastVisionModel, model): model_1 = FastVisionModel.get_peft_model( model, finetune_vision_layers=True, # False if not finetuning vision layers finetune_language_layers=True, # False if not finetuning language layers finetune_attention_modules=True, # False if not finetuning attention layers finetune_mlp_modules=True, # False if not finetuning MLP layers r=16, # The larger, the higher the accuracy, but might overfit lora_alpha=16, # Recommended alpha == r at least lora_dropout=0, bias="none", random_state=3407, use_rslora=False, # We support rank stabilized LoRA loftq_config=None, # And LoftQ ) return (model_1,) @app.cell(hide_code=True) def _(mo): mo.md(r""" ### Data Prep We'll be using a sampled version of the ROCO radiography dataset. You can access the dataset [here](https://huggingface.co/datasets/unsloth/Radiology_mini). The full dataset is [here](https://huggingface.co/datasets/eltorio/ROCOv2-radiology). The dataset includes X-rays, CT scans and ultrasounds showcasing medical conditions and diseases. Each image has a caption written by experts describing it. The goal is to finetune a VLM to make it a useful analysis tool for medical professionals. """) return @app.cell def _(): from datasets import load_dataset dataset = load_dataset("unsloth/Radiology_mini", split="train") return (dataset,) @app.cell(hide_code=True) def _(mo): mo.md(r""" Let's take a look at the dataset, and check what the 1st example shows: """) return @app.cell def _(dataset): dataset return @app.cell def _(dataset): dataset[0]["image"] return @app.cell def _(dataset): dataset[0]["caption"] return @app.cell(hide_code=True) def _(mo): mo.md(r""" To format the dataset, all vision finetuning tasks should be formatted as follows: ```python [ { "role": "user", "content": [{"type": "text", "text": instruction}, {"type": "image", "image": image} ] }, { "role": "assistant", "content": [{"type": "text", "text": answer} ] }, ] ``` We will craft an custom instruction asking the VLM to be an expert radiographer. Notice also instead of just 1 instruction, you can add multiple turns to make it a dynamic conversation. """) return @app.cell def _(): instruction = "You are an expert radiographer. Describe accurately what you see in this image." def convert_to_conversation(sample): conversation = [ { "role": "user", "content": [ {"type": "text", "text": instruction}, {"type": "image", "image": sample["image"]}, ], }, { "role": "assistant", "content": [{"type": "text", "text": sample["caption"]}], }, ] return {"messages": conversation} pass return (convert_to_conversation,) @app.cell(hide_code=True) def _(mo): mo.md(r""" Let's convert the dataset into the "correct" format for finetuning: """) return @app.cell def _(convert_to_conversation, dataset): converted_dataset = [convert_to_conversation(sample) for sample in dataset] return (converted_dataset,) @app.cell(hide_code=True) def _(mo): mo.md(r""" The first example is now structured like below: """) return @app.cell def _(converted_dataset): converted_dataset[0] return @app.cell(hide_code=True) def _(mo): mo.md(r""" Before we do any finetuning, maybe the vision model already knows how to analyse the images? Let's check if this is the case! """) return @app.cell def _(FastVisionModel, dataset, model_1, tokenizer): FastVisionModel.for_inference(model_1) # Enable for inference! image = dataset[0]["image"] instruction_1 = "You are an expert radiographer. Describe accurately what you see in this image." messages = [ { "role": "user", "content": [{"type": "image"}, {"type": "text", "text": instruction_1}], } ] input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True) inputs = tokenizer( image, input_text, add_special_tokens=False, return_tensors="pt" ).to("cuda") from transformers import TextStreamer text_streamer = TextStreamer(tokenizer, skip_prompt=True) _ = model_1.generate( **inputs, streamer=text_streamer, max_new_tokens=128, use_cache=True, temperature=1.5, min_p=0.1, ) return (TextStreamer,) @app.cell(hide_code=True) def _(mo): mo.md(r""" ### Train the model Now let's train our model. 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`. We also support `DPOTrainer` and `GRPOTrainer` for reinforcement learning!! We use our new `UnslothVisionDataCollator` which will help in our vision finetuning setup. """) return @app.cell def _(FastVisionModel, converted_dataset, model_1, tokenizer): from unsloth.trainer import UnslothVisionDataCollator from trl import SFTTrainer, SFTConfig FastVisionModel.for_training(model_1) trainer = SFTTrainer( model=model_1, tokenizer=tokenizer, data_collator=UnslothVisionDataCollator(model_1, tokenizer), # Must use! train_dataset=converted_dataset, args=SFTConfig( per_device_train_batch_size=2, gradient_accumulation_steps=4, warmup_steps=5, max_steps=30, learning_rate=0.0002, logging_steps=1, optim="adamw_8bit", weight_decay=0.001, lr_scheduler_type="linear", seed=3407, output_dir="outputs", report_to="none", # For Weights and Biases remove_unused_columns=False, dataset_text_field="", dataset_kwargs={"skip_prepare_dataset": True}, max_length=2048, ), ) return (trainer,) @app.cell def _(torch): # @title Show current memory stats gpu_stats = torch.cuda.get_device_properties(0) start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3) max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3) print(f"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.") print(f"{start_gpu_memory} GB of memory reserved.") return max_memory, start_gpu_memory @app.cell def _(trainer): trainer_stats = trainer.train() return (trainer_stats,) @app.cell def _(max_memory, start_gpu_memory, torch, trainer_stats): # @title Show final memory and time stats used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3) used_memory_for_lora = round(used_memory - start_gpu_memory, 3) used_percentage = round(used_memory / max_memory * 100, 3) lora_percentage = round(used_memory_for_lora / max_memory * 100, 3) print(f"{trainer_stats.metrics['train_runtime']} seconds used for training.") print( f"{round(trainer_stats.metrics['train_runtime'] / 60, 2)} minutes used for training." ) print(f"Peak reserved memory = {used_memory} GB.") print(f"Peak reserved memory for training = {used_memory_for_lora} GB.") print(f"Peak reserved memory % of max memory = {used_percentage} %.") print(f"Peak reserved memory for training % of max memory = {lora_percentage} %.") return @app.cell(hide_code=True) def _(mo): mo.md(r""" ### Inference Let's run the model! You can change the instruction and input - leave the output blank! We use `min_p = 0.1` and `temperature = 1.5`. Read this [Tweet](https://x.com/menhguin/status/1826132708508213629) for more information on why. """) return @app.cell def _(FastVisionModel, TextStreamer, dataset, model_1, tokenizer): FastVisionModel.for_inference(model_1) # Enable for inference! image_1 = dataset[0]["image"] instruction_2 = "You are an expert radiographer. Describe accurately what you see in this image." messages_1 = [ { "role": "user", "content": [{"type": "image"}, {"type": "text", "text": instruction_2}], } ] input_text_1 = tokenizer.apply_chat_template(messages_1, add_generation_prompt=True) inputs_1 = tokenizer( image_1, input_text_1, add_special_tokens=False, return_tensors="pt" ).to("cuda") text_streamer_1 = TextStreamer(tokenizer, skip_prompt=True) _ = model_1.generate( **inputs_1, streamer=text_streamer_1, max_new_tokens=128, use_cache=True, temperature=1.5, min_p=0.1, ) return @app.cell(hide_code=True) def _(mo): mo.md(r""" ### Saving, loading finetuned models To save the final model as LoRA adapters, either use Hugging Face's `push_to_hub` for an online save or `save_pretrained` for a local save. **[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down! """) return @app.cell def _(model_1, tokenizer): model_1.save_pretrained("llama_lora") tokenizer.save_pretrained("llama_lora") return @app.cell(hide_code=True) def _(mo): mo.md(r""" Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`: """) return @app.cell def _(TextStreamer, dataset, model_1, tokenizer): if False: from unsloth import FastVisionModel as _FastVisionModel _model, _tokenizer = _FastVisionModel.from_pretrained( model_name="llama_lora", load_in_4bit=True # YOUR MODEL YOU USED FOR TRAINING ) _FastVisionModel.for_inference(_model) image_2 = dataset[0]["image"] instruction_3 = "You are an expert radiographer. Describe accurately what you see in this image." messages_2 = [ { "role": "user", "content": [{"type": "image"}, {"type": "text", "text": instruction_3}], } ] input_text_2 = tokenizer.apply_chat_template(messages_2, add_generation_prompt=True) inputs_2 = tokenizer( image_2, input_text_2, add_special_tokens=False, return_tensors="pt" ).to("cuda") text_streamer_2 = TextStreamer(tokenizer, skip_prompt=True) _ = model_1.generate( **inputs_2, streamer=text_streamer_2, max_new_tokens=128, use_cache=True, temperature=1.5, min_p=0.1, ) return @app.cell(hide_code=True) def _(mo): mo.md(r""" ### Saving to float16 for VLLM We also support saving to `float16` directly. Select `merged_16bit` for float16. 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](https://unsloth.ai/docs/basics/inference-and-deployment) for more deployment options. """) return @app.cell def _(model_1, tokenizer): # Select ONLY 1 to save! (Both not needed!) if False: # Save locally to 16bit model_1.save_pretrained_merged("unsloth_finetune", tokenizer) if False: # To export and save to your Hugging Face account model_1.push_to_hub_merged( "YOUR_USERNAME/unsloth_finetune", tokenizer, token="YOUR_HF_TOKEN" ) return @app.cell(hide_code=True) def _(mo): mo.md(r""" And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) 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: 1. Looking to use Unsloth locally? Read our [Installation Guide](https://unsloth.ai/docs/get-started/install) for details on installing Unsloth on Windows, Docker, AMD, Intel GPUs. 2. Learn how to do Reinforcement Learning with our [RL Guide and notebooks](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide). 3. Read our guides and notebooks for [Text-to-speech (TTS)](https://unsloth.ai/docs/basics/text-to-speech-tts-fine-tuning) and [vision](https://unsloth.ai/docs/basics/vision-fine-tuning) model support. 4. Explore our [LLM Tutorials Directory](https://unsloth.ai/docs/models/tutorials-how-to-fine-tune-and-run-llms) to find dedicated guides for each model. 5. Need help with Inference? Read our [Inference & Deployment page](https://unsloth.ai/docs/basics/inference-and-deployment) for details on using vLLM, llama.cpp, Ollama etc.

Join Discord if you need help + ⭐️ Star us on Github ⭐️ This notebook and all Unsloth notebooks are licensed [LGPL-3.0](https://github.com/unslothai/notebooks?tab=LGPL-3.0-1-ov-file#readme)

""") return if __name__ == "__main__": app.run()