# /// 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`.

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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""" ### Unsloth """) return @app.cell def _(): from unsloth import FastLanguageModel from unsloth.chat_templates import ( get_chat_template, ) import torch MODEL_ID = "unsloth/Qwen3-0.6B" QAT_SCHEME = "int8-int4" model, tokenizer = FastLanguageModel.from_pretrained( model_name=MODEL_ID, max_seq_length=2048, dtype=torch.bfloat16, load_in_4bit=False, full_finetuning=True, # ExecuTorch CPU quantization scheme # Quantize embedding to 8-bits, and quantize linear layers to 4-bits # with 8-bit dynamically quantized activations qat_scheme=QAT_SCHEME, ) tokenizer = get_chat_template(tokenizer, chat_template="qwen3") return MODEL_ID, QAT_SCHEME, model, tokenizer @app.cell(hide_code=True) def _(mo): mo.md(r""" ### Data Prep Qwen3 has both reasoning and a non reasoning mode. So, we should use 2 datasets: 1. We use the [Open Math Reasoning](https://huggingface.co/datasets/unsloth/OpenMathReasoning-mini) dataset which was used to win the [AIMO](https://www.kaggle.com/competitions/ai-mathematical-olympiad-progress-prize-2/leaderboard) (AI Mathematical Olympiad - Progress Prize 2) challenge! We sample 10% of verifiable reasoning traces that used DeepSeek R1, and which got > 95% accuracy. 2. We also leverage [Maxime Labonne's FineTome-100k](https://huggingface.co/datasets/mlabonne/FineTome-100k) dataset in ShareGPT style. But we need to convert it to HuggingFace's normal multiturn format as well. """) return @app.cell def _(): from datasets import load_dataset reasoning_dataset = load_dataset("unsloth/OpenMathReasoning-mini", split="cot") non_reasoning_dataset = load_dataset("mlabonne/FineTome-100k", split="train") return non_reasoning_dataset, reasoning_dataset @app.cell(hide_code=True) def _(mo): mo.md(r""" Let's see the structure of both datasets: """) return @app.cell def _(reasoning_dataset): reasoning_dataset return @app.cell def _(non_reasoning_dataset): non_reasoning_dataset return @app.cell(hide_code=True) def _(mo): mo.md(r""" We now convert the reasoning dataset into conversational format: """) return @app.function def generate_conversation(examples): problems = examples["problem"] solutions = examples["generated_solution"] conversations = [] for problem, solution in zip(problems, solutions): conversations.append( [ {"role": "user", "content": problem}, {"role": "assistant", "content": solution}, ] ) return { "conversations": conversations, } @app.cell def _(reasoning_dataset, tokenizer): reasoning_conversations = tokenizer.apply_chat_template( list( reasoning_dataset.map(generate_conversation, batched=True)["conversations"] ), tokenize=False, ) return (reasoning_conversations,) @app.cell(hide_code=True) def _(mo): mo.md(r""" Let's see the first transformed row: """) return @app.cell def _(reasoning_conversations): reasoning_conversations[0] return @app.cell(hide_code=True) def _(mo): mo.md(r""" Next we take the non reasoning dataset and convert it to conversational format as well. We have to use Unsloth's `standardize_sharegpt` function to fix up the format of the dataset first. """) return @app.cell def _(non_reasoning_dataset, tokenizer): from unsloth.chat_templates import standardize_sharegpt dataset = standardize_sharegpt(non_reasoning_dataset) non_reasoning_conversations = tokenizer.apply_chat_template( list(dataset["conversations"]), tokenize=False, ) return (non_reasoning_conversations,) @app.cell(hide_code=True) def _(mo): mo.md(r""" Let's see the first row """) return @app.cell def _(non_reasoning_conversations): non_reasoning_conversations[0] return @app.cell(hide_code=True) def _(mo): mo.md(r""" Now let's see how long both datasets are: """) return @app.cell def _(non_reasoning_conversations, reasoning_conversations): print(len(reasoning_conversations)) print(len(non_reasoning_conversations)) return @app.cell(hide_code=True) def _(mo): mo.md(r""" The non reasoning dataset is much longer. Let's assume we want the model to retain some reasoning capabilities, but we specifically want a chat model. Let's define a ratio of chat only data. The goal is to define some mixture of both sets of data. Let's select 75% reasoning and 25% chat based: """) return @app.cell def _(): chat_percentage = 0.25 return (chat_percentage,) @app.cell(hide_code=True) def _(mo): mo.md(r""" Let's sample the reasoning dataset by 75% (or whatever is 100% - chat_percentage) """) return @app.cell def _(chat_percentage, non_reasoning_conversations, reasoning_conversations): import pandas as pd non_reasoning_subset = pd.Series(non_reasoning_conversations) non_reasoning_subset = non_reasoning_subset.sample( int(len(reasoning_conversations) * (chat_percentage / (1 - chat_percentage))), random_state=2407, ) print(len(reasoning_conversations)) print(len(non_reasoning_subset)) print( len(non_reasoning_subset) / (len(non_reasoning_subset) + len(reasoning_conversations)) ) return non_reasoning_subset, pd @app.cell(hide_code=True) def _(mo): mo.md(r""" Finally combine both datasets: """) return @app.cell def _(non_reasoning_subset, pd, reasoning_conversations): data = pd.concat( [pd.Series(reasoning_conversations), pd.Series(non_reasoning_subset)] ) data.name = "text" from datasets import Dataset combined_dataset = Dataset.from_pandas(pd.DataFrame(data)) combined_dataset = combined_dataset.shuffle(seed=3407) return (combined_dataset,) @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`. """) return @app.cell def _(combined_dataset, model, tokenizer): from trl import SFTTrainer, SFTConfig trainer = SFTTrainer( model=model, tokenizer=tokenizer, train_dataset=combined_dataset, eval_dataset=None, # Can set up evaluation! args=SFTConfig( dataset_text_field="text", per_device_train_batch_size=2, gradient_accumulation_steps=4, # Use GA to mimic batch size! warmup_steps=5, # num_train_epochs = 1, # Set this for 1 full training run. max_steps=100, learning_rate=5e-5, logging_steps=1, optim="adamw_8bit", weight_decay=0.001, lr_scheduler_type="linear", seed=3407, report_to="none", # Use TrackIO/WandB etc ), ) return (trainer,) @app.cell(hide_code=True) def _(mo): mo.md(r""" Let's train the model! To resume a training run, set `trainer.train(resume_from_checkpoint = True)` """) return @app.cell def _(trainer): trainer_stats = trainer.train() return @app.cell(hide_code=True) def _(mo): mo.md(r""" ### Inference Let's run the model via Unsloth native inference! According to the `Qwen-3` team, the recommended settings for reasoning inference are `temperature = 0.6, top_p = 0.95, top_k = 20` For normal chat based inference, `temperature = 0.7, top_p = 0.8, top_k = 20` """) return @app.cell def _(model, tokenizer): messages = [{"role": "user", "content": "Solve (x + 2)^2 = 0."}] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, # Must add for generation enable_thinking=False, # Disable thinking ) from transformers import TextStreamer _ = model.generate( **tokenizer(text, return_tensors="pt").to("cuda"), max_new_tokens=256, # Increase for longer outputs! temperature=0.7, # For non thinking top_p=0.8, top_k=20, # For non thinking streamer=TextStreamer(tokenizer, skip_prompt=True), ) return (TextStreamer,) @app.cell def _(TextStreamer, model, tokenizer): messages_1 = [{"role": "user", "content": "Solve (x + 2)^2 = 0."}] text_1 = tokenizer.apply_chat_template( messages_1, tokenize=False, add_generation_prompt=True, enable_thinking=True ) _ = model.generate( **tokenizer(text_1, return_tensors="pt").to("cuda"), max_new_tokens=1024, # Increase for longer outputs! temperature=0.6, # For non thinking top_p=0.95, top_k=20, streamer=TextStreamer(tokenizer, skip_prompt=True), ) return @app.cell(hide_code=True) def _(mo): mo.md(r""" ### Saving The final model can be saved locally or pushed to the Hub to share with the community. """) return @app.cell def _(MODEL_ID, QAT_SCHEME, model, tokenizer): model_name = MODEL_ID.split("/")[-1] save_to = f"{model_name}-{QAT_SCHEME}-unsloth" # Save locally - this auto-detects QAT models and handles the conversion model.save_pretrained_torchao(save_to, tokenizer=tokenizer) return @app.cell(hide_code=True) def _(mo): mo.md(r""" Once saved, we can export the model checkpoint to ExecuTorch. 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()