# /// script # requires-python = ">=3.10,<3.14" # dependencies = [ # "bitsandbytes>=0.43.0", # "marimo", # "tokenizers>=0.22.0,<=0.23.0", # "torch>=2.8.0", # "torchao>=0.16.0", # "torchvision", # "transformers==4.56.2", # "triton>=3.2.0", # "triton_kernels @ git+https://github.com/triton-lang/triton.git@0add68262ab0a2e33b84524346cb27cbb2787356#subdirectory=python/triton_kernels", # "trl==0.22.2", # "unsloth @ git+https://github.com/unslothai/unsloth", # "unsloth_zoo @ git+https://github.com/unslothai/unsloth-zoo", # "uv", # ] # # [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 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(hide_code=True) def _(mo): mo.md(r""" We're about to demonstrate the power of the new OpenAI GPT-OSS 20B model through an inference example. For our `bnb-4bit` version, use this [notebook](https://github.com/unslothai/notebooks/blob/main/nb/GPT_OSS_BNB_(20B)-Inference.ipynb) instead. **We're using OpenAI's MXFP4 Triton kernels combined with Unsloth's kernels!** """) return @app.cell def _(): from unsloth import FastLanguageModel import torch # 4bit pre quantized models we support for 4x faster downloading + no OOMs. fourbit_models = [ "unsloth/gpt-oss-20b-unsloth-bnb-4bit", # 20B model using bitsandbytes 4bit quantization "unsloth/gpt-oss-120b-unsloth-bnb-4bit", "unsloth/gpt-oss-20b", # 20B model using MXFP4 format "unsloth/gpt-oss-120b", ] # More models at https://huggingface.co/unsloth model, tokenizer = FastLanguageModel.from_pretrained( model_name="unsloth/gpt-oss-20b", dtype=None, # None for auto detection max_seq_length=4096, # Choose any for long context! load_in_4bit=False, # 4 bit quantization to reduce memory 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""" ### Reasoning Effort The `gpt-oss` models from OpenAI include a feature that allows users to adjust the model's "reasoning effort." This gives you control over the trade-off between the model's performance and its response speed (latency) which by the amount of token the model will use to think. ---- The `gpt-oss` models offer three distinct levels of reasoning effort you can choose from: * **Low**: Optimized for tasks that need very fast responses and don't require complex, multi-step reasoning. * **Medium**: A balance between performance and speed. * **High**: Provides the strongest reasoning performance for tasks that require it, though this results in higher latency. """) return @app.cell def _(model, tokenizer): from transformers import TextStreamer messages = [ {"role": "user", "content": "Solve x^5 + 3x^4 - 10 = 3."}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt", return_dict=True, reasoning_effort="low", # **NEW!** Set reasoning effort to low, medium or high ).to("cuda") _ = model.generate(**inputs, max_new_tokens=512, streamer=TextStreamer(tokenizer)) return (TextStreamer,) @app.cell(hide_code=True) def _(mo): mo.md(r""" Changing the `reasoning_effort` to `medium` will make the model think longer. We have to increase the `max_new_tokens` to occupy the amount of the generated tokens but it will give better and more correct answer """) return @app.cell def _(TextStreamer, model, tokenizer): messages_1 = [{"role": "user", "content": "Solve x^5 + 3x^4 - 10 = 3."}] inputs_1 = tokenizer.apply_chat_template( messages_1, add_generation_prompt=True, return_tensors="pt", return_dict=True, reasoning_effort="medium", # **NEW!** Set reasoning effort to low, medium or high ).to("cuda") _ = model.generate( **inputs_1, max_new_tokens=1024, streamer=TextStreamer(tokenizer) ) # **NEW!** Set reasoning effort to low, medium or high return @app.cell(hide_code=True) def _(mo): mo.md(r""" Lastly we will test it using `reasoning_effort` to `high` """) return @app.cell def _(TextStreamer, model, tokenizer): messages_2 = [{"role": "user", "content": "Solve x^5 + 3x^4 - 10 = 3."}] inputs_2 = tokenizer.apply_chat_template( messages_2, add_generation_prompt=True, return_tensors="pt", return_dict=True, reasoning_effort="high", # **NEW!** Set reasoning effort to low, medium or high ).to("cuda") _ = model.generate( **inputs_2, max_new_tokens=2048, streamer=TextStreamer(tokenizer) ) # **NEW!** Set reasoning effort to low, medium or high 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()