# /// 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"""

""") return @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 FastLanguageModel import torch max_seq_length = 4096 # Can choose any sequence length! fourbit_models = [ # 4bit Gemma 3 dynamic quants for superior accuracy and low memory use "unsloth/gemma-3-270m-it-unsloth-bnb-4bit", "unsloth/gemma-3-1b-it-unsloth-bnb-4bit", "unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "unsloth/gemma-3-12b-it-unsloth-bnb-4bit", "unsloth/gemma-3-27b-it-unsloth-bnb-4bit", # Function Gemma models "unsloth/functiongemma-270m-it", "unsloth/functiongemma-270m-it-unsloth-bnb-4bit", "unsloth/functiongemma-270m-it-bnb-4bit", ] # More models at https://huggingface.co/unsloth model, tokenizer = FastLanguageModel.from_pretrained( model_name="unsloth/functiongemma-270m-it", max_seq_length=max_seq_length, # Choose any for long context! load_in_4bit=False, # 4 bit quantization to reduce memory load_in_8bit=False, # [NEW!] A bit more accurate, uses 2x memory load_in_16bit=True, # [NEW!] Enables 16bit LoRA full_finetuning=False, # [NEW!] We have full finetuning now! # token = "YOUR_HF_TOKEN", # HF Token for gated models ) return model, tokenizer @app.cell(hide_code=True) def _(mo): mo.md(r""" # Define tools for FunctionGemma We'll define multiple tools which FunctionGemma can call, including the below: ``` get_today_date get_current_weather add_numbers multiply_numbers ``` """) return @app.cell def _(): def get_today_date(): """ Gets today's date Returns: today_date: Today's date in format 18 December 2025 """ from datetime import datetime today_date = datetime.today().strftime("%d %B %Y") return {"today_date": today_date} def get_current_weather(location: str, unit: str = "celsius"): """ Gets the current weather in a given location. Args: location: The city and state, e.g. "San Francisco, CA, USA" or "Sydney, Australia" unit: The unit to return the temperature in. (choices: ["celsius", "fahrenheit"]) Returns: temperature: The current temperature in the given location weather: The current weather in the given location """ if "San Francisco" in location.title(): return {"temperature": 15, "weather": "sunny"} elif "Sydney" in location.title(): return {"temperature": 25, "weather": "cloudy"} else: return {"temperature": 30, "weather": "rainy"} def add_numbers(x: float | str, y: float | str): """ Adds 2 numbers together Args: x: First number y: Second number Returns: result: x + y """ return {"result": float(x) + float(y)} def multiply_numbers(x: float | str, y: float | str): """ Multiplies 2 numbers together Args: x: First number y: Second number Returns: result: x * y """ return {"result": float(x) * float(y)} return add_numbers, get_current_weather, get_today_date, multiply_numbers @app.cell(hide_code=True) def _(mo): mo.md(r""" We save all the functions to a mapping and as tools """) return @app.cell def _(add_numbers, get_current_weather, get_today_date, multiply_numbers): FUNCTION_MAPPING = { "get_today_date": get_today_date, "get_current_weather": get_current_weather, "add_numbers": add_numbers, "multiply_numbers": multiply_numbers, } TOOLS = list(FUNCTION_MAPPING.values()) return FUNCTION_MAPPING, TOOLS @app.cell(hide_code=True) def _(mo): mo.md(r""" We then make some parsing code for FunctionGemma which we hide """) return @app.cell def _(FUNCTION_MAPPING, TOOLS, tokenizer): # @title FunctionGemma parsing code (expandible) import re def extract_tool_calls(text): def cast(v): try: return int(v) except: try: return float(v) except: return {"true": True, "false": False}.get(v.lower(), v.strip("'\"")) return [ { "name": name, "arguments": { k: cast((v1 or v2).strip()) for k, v1, v2 in re.findall( r"(\w+):(?:(.*?)|([^,}]*))", args ) }, } for name, args in re.findall( r"call:(\w+)\{(.*?)\}", text, re.DOTALL, ) ] def process_tool_calls(output, messages): calls = extract_tool_calls(output) if not calls: return messages messages.append( { "role": "assistant", "tool_calls": [ {"type": "function", "function": call} for call in calls ], } ) results = [ { "name": c["name"], "response": FUNCTION_MAPPING[c["name"]](**c["arguments"]), } for c in calls ] messages.append({"role": "tool", "content": results}) return messages def _do_inference(model, messages, max_new_tokens=128): inputs = tokenizer.apply_chat_template( messages, tools=TOOLS, add_generation_prompt=True, return_dict=True, return_tensors="pt", ) output = tokenizer.decode(inputs["input_ids"][0], skip_special_tokens=False) out = model.generate( **inputs.to(model.device), max_new_tokens=max_new_tokens, top_p=0.95, top_k=64, temperature=1.0, ) generated_tokens = out[0][len(inputs["input_ids"][0]) :] return tokenizer.decode(generated_tokens, skip_special_tokens=True) def do_inference(model, messages, print_assistant=True, max_new_tokens=128): output = _do_inference(model, messages, max_new_tokens=max_new_tokens) messages = process_tool_calls(output, messages) if messages[-1]["role"] == "tool": output = _do_inference(model, messages, max_new_tokens=max_new_tokens) messages.append({"role": "assistant", "content": output}) if print_assistant: print(output) return messages return (do_inference,) @app.cell(hide_code=True) def _(mo): mo.md(r""" Let's call the model! """) return @app.cell def _(do_inference, model): messages = [] messages.append({"role": "user", "content": "What's today's date?"}) messages = do_inference(model, messages, max_new_tokens=128) return (messages,) @app.cell def _(do_inference, messages, model): messages.append( {"role": "user", "content": "What's the weather like in San Francisco?"} ) messages_1 = do_inference(model, messages, max_new_tokens=128) return (messages_1,) @app.cell def _(do_inference, messages_1, model): messages_1.append( {"role": "user", "content": "What's the weather like in Sydney, Australia?"} ) messages_2 = do_inference(model, messages_1, max_new_tokens=128) return (messages_2,) @app.cell def _(do_inference, messages_2, model): messages_2.append({"role": "user", "content": "Add 112358 and 123456"}) messages_3 = do_inference(model, messages_2, max_new_tokens=128) return (messages_3,) @app.cell def _(do_inference, messages_3, model): messages_3.append({"role": "user", "content": "Multiply 112358 and 123456"}) messages_4 = do_inference(model, messages_3, max_new_tokens=128) return (messages_4,) @app.cell def _(do_inference, messages_4, model): messages_4.append({"role": "user", "content": "Do the addition of 2 and 231.111"}) messages_5 = do_inference(model, messages_4, max_new_tokens=128) 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. 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()