# /// 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==5.3.0", # "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 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(hide_code=True) def _(mo): mo.md(r""" Goal: To demonstrate unsloth's MoE optimizations on T4 for `imdatta0/tiny_qwen3_moe_2.8B_0.7B` by finetuning on by OpenR1's Math dataset. """) return @app.cell def _(): model_name = "imdatta0/tiny_qwen3_moe_2.8B_0.7B" # This is dummy model of qwen3moe architecture created to fit in T4 max_seq_length = 2048 # Can increase for longer reasoning traces lora_rank = 32 # Larger rank = smarter, but slower return lora_rank, max_seq_length, model_name @app.cell def _(lora_rank, max_seq_length, model_name): from unsloth import FastLanguageModel import torch model, tokenizer = FastLanguageModel.from_pretrained( model_name, max_seq_length=max_seq_length, # Can increase for longer reasoning traces load_in_4bit=False, fast_inference=False, # Not supported for MoE (yet!) ) model = FastLanguageModel.get_peft_model( model, r=lora_rank, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128 target_modules=[ "q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj", "gate_up_proj", # Enable LoRA on MoE layers ], lora_alpha=lora_rank * 2, # *2 speeds up training use_gradient_checkpointing=True, # Reduces memory usage random_state=3407, bias="none", ) return model, tokenizer, torch @app.cell(hide_code=True) def _(mo): mo.md(r""" ### GRPO chat template Since we're using a base model, we should set a chat template. You can make your own chat template as well! 1. DeepSeek uses `` and ``, but this is **not** necessary - you can customize it however you like! 2. A `system_prompt` is recommended to at least guide the model's responses. """) return @app.cell def _(): reasoning_start = "" # Acts as think-open tag reasoning_end = "" # Acts as think-close tag solution_start = "" solution_end = "" system_prompt = f"""You are given a problem. Think about the problem and provide your working out. Place it between {reasoning_start} and {reasoning_end}. Then, provide your solution between {solution_start}{solution_end}""" system_prompt return ( reasoning_end, reasoning_start, solution_end, solution_start, system_prompt, ) @app.cell(hide_code=True) def _(mo): mo.md(r""" We create a simple chat template below. Notice `add_generation_prompt` includes prepending `` to guide the model to start its reasoning process. """) return @app.cell def _(reasoning_start, system_prompt, tokenizer): chat_template = ( "{% if messages[0]['role'] == 'system' %}" "{{ messages[0]['content'] + eos_token }}" "{% set loop_messages = messages[1:] %}" "{% else %}" "{{ '{system_prompt}' + eos_token }}" "{% set loop_messages = messages %}" "{% endif %}" "{% for message in loop_messages %}" "{% if message['role'] == 'user' %}" "{{ message['content'] }}" "{% elif message['role'] == 'assistant' %}" "{{ message['content'] + eos_token }}" "{% endif %}" "{% endfor %}" "{% if add_generation_prompt %}{{ '{reasoning_start}' }}" "{% endif %}" ) # Replace with our specific template: chat_template = chat_template.replace( "'{system_prompt}'", f"'{system_prompt}'" ).replace("'{reasoning_start}'", f"'{reasoning_start}'") tokenizer.chat_template = chat_template return @app.cell(hide_code=True) def _(mo): mo.md(r""" Let's see how our chat template behaves on an example: """) return @app.cell def _(reasoning_end, reasoning_start, solution_end, solution_start, tokenizer): tokenizer.apply_chat_template( [ {"role": "user", "content": "What is 1+1?"}, { "role": "assistant", "content": f"{reasoning_start}I think it's 2.{reasoning_end}{solution_start}2{solution_end}", }, {"role": "user", "content": "What is 2+2?"}, ], tokenize=False, add_generation_prompt=True, # Must add for generation ) return @app.cell(hide_code=True) def _(mo): mo.md(r""" ### Pre fine-tuning for formatting We now use a subset of NVIDIA's [Open Math Reasoning dataset](https://huggingface.co/datasets/nvidia/OpenMathReasoning) which was filtered to only include high quality DeepSeek R1 traces. We'll only filter ~59 or so examples to first "prime" / pre fine-tune the model to understand our custom GRPO formatting. """) return @app.cell def _(): from datasets import load_dataset import pandas as pd import numpy as np dataset = load_dataset("unsloth/OpenMathReasoning-mini", split="cot") dataset = dataset.to_pandas()[["expected_answer", "problem", "generated_solution"]] # Try converting to number - if not, replace with NaN is_number = pd.to_numeric( pd.Series(dataset["expected_answer"]), errors="coerce" ).notnull() # Select only numbers dataset = dataset.iloc[np.where(is_number)[0]] dataset return (dataset,) @app.cell(hide_code=True) def _(mo): mo.md(r""" We have to format the dataset to follow our GRPO style formatting: """) return @app.cell def _( dataset, reasoning_end, reasoning_start, solution_end, solution_start, system_prompt, ): def format_dataset(x): expected_answer = x["expected_answer"] problem = x["problem"] # Remove generated think tags thoughts = x["generated_solution"] thoughts = thoughts.replace("", "").replace("", "") # Strip newlines on left and right thoughts = thoughts.strip() # Add our custom formatting final_prompt = ( reasoning_start + thoughts + reasoning_end + solution_start + expected_answer + solution_end ) return [ {"role": "system", "content": system_prompt}, {"role": "user", "content": problem}, {"role": "assistant", "content": final_prompt}, ] dataset["Messages"] = dataset.apply(format_dataset, axis=1) return @app.cell(hide_code=True) def _(mo): mo.md(r""" Check to see if it worked: """) return @app.cell def _(dataset, tokenizer): tokenizer.apply_chat_template(dataset["Messages"][0], tokenize=False) return @app.cell(hide_code=True) def _(mo): mo.md(r""" Let's truncate the pre fine-tuning dataset to `max_seq_length/2` since we don't want too long reasoning traces. Note this might take 2 minutes! """) return @app.cell def _(dataset, max_seq_length, tokenizer): dataset["N"] = dataset["Messages"].apply( lambda x: len(tokenizer.apply_chat_template(x)["input_ids"]) ) dataset_1 = dataset.loc[dataset["N"] <= max_seq_length / 2].copy() dataset_1.shape return (dataset_1,) @app.cell(hide_code=True) def _(mo): mo.md(r""" We then tokenize the messages and convert it to a Hugging Face compatible dataset format: """) return @app.cell def _(dataset_1, tokenizer): from datasets import Dataset dataset_1["text"] = tokenizer.apply_chat_template( dataset_1["Messages"].values.tolist(), tokenize=False ) dataset_2 = Dataset.from_pandas(dataset_1) dataset_2 return (dataset_2,) @app.cell(hide_code=True) def _(mo): mo.md(r""" Let's now pre fine-tune the model so it follows our custom GRPO formatting! """) return @app.cell def _(dataset_2, model, tokenizer): from trl import SFTTrainer, SFTConfig trainer = SFTTrainer( model=model, tokenizer=tokenizer, train_dataset=dataset_2, args=SFTConfig( dataset_text_field="text", per_device_train_batch_size=1, gradient_accumulation_steps=1, # Use GA to mimic batch size! warmup_steps=5, max_steps=50, learning_rate=0.0002, # Reduce to 2e-5 for long training runs logging_steps=5, optim="adamw_8bit", weight_decay=0.001, lr_scheduler_type="linear", seed=3407, report_to="none", # Use TrackIO/WandB etc ), ) return (trainer,) @app.cell def _(trainer): trainer.train() return @app.cell(hide_code=True) def _(mo): mo.md(r""" The model would generate gibberish here as we initialized a dummy model from random weights. Prolonged training can help but this is just for a demo. """) return @app.cell def _(dataset_2, model, tokenizer): text = tokenizer.apply_chat_template( dataset_2[0]["Messages"][:2], tokenize=False, add_generation_prompt=True ) from transformers import TextStreamer _ = model.generate( **tokenizer(text, return_tensors="pt").to("cuda"), temperature=0.1, max_new_tokens=128, streamer=TextStreamer(tokenizer, skip_prompt=False), use_cache=True, ) # Must add for generation return @app.cell(hide_code=True) def _(mo): mo.md(r""" Yes it did follow the formatting! Great! Let's remove some items before the GRPO step """) return @app.cell def _(dataset_2, torch): del dataset_2 torch.cuda.empty_cache() import gc gc.collect() 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 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. 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, tokenizer): # Merge to 16bit if False: model.save_pretrained_merged( "tinyqwen_finetune_16bit", tokenizer, save_method="merged_16bit", ) if False: model.push_to_hub_merged( "HF_USERNAME/tinyqwen_finetune_16bit", tokenizer, save_method="merged_16bit", token="YOUR_HF_TOKEN", ) # Merge to 4bit if False: model.save_pretrained_merged( "tinyqwen_finetune_4bit", tokenizer, save_method="merged_4bit", ) if False: model.push_to_hub_merged( "HF_USERNAME/tinyqwen_finetune_4bit", tokenizer, save_method="merged_4bit", token="YOUR_HF_TOKEN", ) # Just LoRA adapters if False: model.save_pretrained("tinyqwen_lora") tokenizer.save_pretrained("tinyqwen_lora") if False: model.push_to_hub("HF_USERNAME/tinyqwen_lora", token="YOUR_HF_TOKEN") tokenizer.push_to_hub("HF_USERNAME/tinyqwen_lora", 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)): * `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, tokenizer): # Save to 8bit Q8_0 if False: model.save_pretrained_gguf( "tinyqwen_finetune", tokenizer, ) if False: model.push_to_hub_gguf( "HF_USERNAME/tinyqwen_finetune", tokenizer, token="YOUR_HF_TOKEN" ) # Save to 16bit GGUF if False: model.save_pretrained_gguf( "tinyqwen_finetune", tokenizer, quantization_method="f16" ) if False: model.push_to_hub_gguf( "HF_USERNAME/tinyqwen_finetune", tokenizer, quantization_method="f16", token="YOUR_HF_TOKEN", ) # Save to q4_k_m GGUF if False: model.save_pretrained_gguf( "tinyqwen_finetune", tokenizer, quantization_method="q4_k_m" ) if False: model.push_to_hub_gguf( "HF_USERNAME/tinyqwen_finetune", tokenizer, quantization_method="q4_k_m", token="YOUR_HF_TOKEN", ) # Save to multiple GGUF options - much faster if you want multiple! if False: model.push_to_hub_gguf( "HF_USERNAME/tinyqwen_finetune", # Change hf to your username! 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 `tinyqwen_finetune.Q8_0.gguf` file or `tinyqwen_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()