# /// 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""" # 💧 LFM2.5-VL - SFT with Unsloth To run this notebook on your L4 molab Pro instance, 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 own computer, follow the installation instructions on our Github page [here](https://unsloth.ai/docs/get-started/install). 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 import torch model, tokenizer = FastVisionModel.from_pretrained( model_name="LiquidAI/LFM2.5-VL-1.6B", # YOUR MODEL YOU USED FOR TRAINING max_seq_length=2048, # Choose any for long context! load_in_4bit=False, # 4 bit quantization to reduce memory ) return FastVisionModel, model, tokenizer, torch @app.cell(hide_code=True) def _(mo): mo.md(r""" We now add LoRA adapters so we only need to update a small amount of parameters! """) return @app.cell def _(FastVisionModel, model): model_1 = FastVisionModel.get_peft_model( model, finetune_vision_layers=False, # Set to False for now 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 dataset of handwritten maths formulas. The goal is to convert these images into a computer readable form - ie in LaTeX form, so we can render it. This can be very useful for complex formulas. You can access the dataset [here](https://huggingface.co/datasets/unsloth/LaTeX_OCR). The full dataset is [here](https://huggingface.co/datasets/linxy/LaTeX_OCR). LFM-VL renders ChatML conversations with images like below: ``` <|startoftext|><|im_start|>system You are a helpful multimodal assistant by Liquid AI.<|im_end|> <|im_start|>user Describe this image.<|im_end|> <|im_start|>assistant This image shows a Caenorhabditis elegans (C. elegans) nematode.<|im_end|> ``` """) return @app.cell def _(tokenizer): tokenizer.apply_chat_template( [ { "role": "user", "content": [ {"type": "image"}, {"type": "text", "text": "What's in this image?"}, ], }, {"role": "assistant", "content": "I can see a cat sitting on a couch."}, ], tokenize=False, ) return @app.cell(hide_code=True) def _(mo): mo.md(r""" We get the first 3000 rows of the dataset """) return @app.cell def _(): from datasets import load_dataset dataset = load_dataset("unsloth/LaTeX_OCR", split="train[:3000]") return (dataset,) @app.cell(hide_code=True) def _(mo): mo.md(r""" Let's take an overview look at the dataset. We shall see what the 3rd image is, and what caption it had. """) return @app.cell def _(dataset): dataset return @app.cell def _(dataset): dataset[2]["image"] return @app.cell def _(dataset): dataset[2]["text"] 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": Q}, {"type": "image", "image": image} ] }, { "role": "assistant", "content": [{"type": "text", "text": A} ] }, ] ``` """) return @app.cell def _(): instruction = "Write the LaTeX representation for 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["text"]}], }, ] 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""" We look at how the conversations are structured for the first example: """) return @app.cell def _(converted_dataset): converted_dataset[0] return @app.cell(hide_code=True) def _(mo): mo.md(r""" Let's first see before we do any finetuning what the model outputs for the first example! """) return @app.cell def _(FastVisionModel, dataset, model_1, tokenizer): FastVisionModel.for_inference(model_1) # Enable for inference! image = dataset[2]["image"] instruction_1 = "Write the LaTeX representation for 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[2]["image"] instruction_2 = "Write the LaTeX representation for 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("lfm_lora") tokenizer.save_pretrained("lfm_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="lfm_lora", load_in_4bit=True # YOUR MODEL YOU USED FOR TRAINING ) _FastVisionModel.for_inference(_model) image_2 = dataset[0]["image"] instruction_3 = "Write the LaTeX representation for 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""" ### GGUF / llama.cpp Conversion To save to `GGUF` / `llama.cpp`, we support it natively now! We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF. Some supported quant methods (full list on our [docs page](https://unsloth.ai/docs/basics/inference-and-deployment/saving-to-gguf#locally)): * `q8_0` - Fast conversion. High resource use, but generally acceptable. * `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K. * `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K. [**NEW**] To finetune and auto export to Ollama, try our [Ollama notebook](https://github.com/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb) """) return @app.cell def _(model_1, tokenizer): # Save to 8bit Q8_0 if False: # Remember to go to https://huggingface.co/settings/tokens for a token! # And change hf to your username! model_1.save_pretrained_gguf("lfm_finetune", tokenizer) if False: # Save to 16bit GGUF model_1.push_to_hub_gguf( "HF_USERNAME/lfm_finetune", tokenizer, token="YOUR_HF_TOKEN" ) if False: model_1.save_pretrained_gguf( "lfm_finetune", tokenizer, quantization_method="f16" ) # Save to q4_k_m GGUF if False: model_1.push_to_hub_gguf( "HF_USERNAME/lfm_finetune", tokenizer, quantization_method="f16", token="YOUR_HF_TOKEN", ) if False: # Save to multiple GGUF options - much faster if you want multiple! model_1.save_pretrained_gguf( "lfm_finetune", tokenizer, quantization_method="q4_k_m" ) if False: model_1.push_to_hub_gguf( "HF_USERNAME/lfm_finetune", tokenizer, quantization_method="q4_k_m", token="YOUR_HF_TOKEN", ) # Change hf to your username! if False: model_1.push_to_hub_gguf( "HF_USERNAME/lfm_finetune", tokenizer, quantization_method=["q4_k_m", "q8_0", "q5_k_m"], token="YOUR_HF_TOKEN", ) return @app.cell(hide_code=True) def _(mo): mo.md(r""" Now, use the `lfm_finetune.Q8_0.gguf` file or `lfm_finetune.Q4_K_M.gguf` file in llama.cpp. 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. Train your own reasoning model - Llama GRPO notebook [Open in molab](https://github.com/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb) 2. Saving finetunes to Ollama. [Free notebook](https://github.com/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb) 3. Llama 3.2 Vision finetuning - Radiography use case. [Open in molab](https://github.com/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb) 4. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://unsloth.ai/docs/get-started/unsloth-notebooks)!

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()