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+{"cells":[{"cell_type":"markdown","id":"M1SokUCUMm-_","metadata":{"id":"M1SokUCUMm-_"},"source":["# Fine-tuning a SLM with HF 🤗"]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')\n","\n","%cd /content/drive/MyDrive/'Learning 📚'/'YouTube ▶️'/'Daniel Bourke'/'gemma3-270m-mermaid'"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"42CV2ooGQSre","executionInfo":{"status":"ok","timestamp":1769700059314,"user_tz":180,"elapsed":132454,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"}},"outputId":"1d57b3db-f4ba-439e-aad0-73671a85c539"},"id":"42CV2ooGQSre","execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n","/content/drive/MyDrive/Learning 📚/YouTube ▶️/Daniel Bourke/gemma3-270m-mermaid\n"]}]},{"cell_type":"markdown","id":"9u31JzuaWsmL","metadata":{"id":"9u31JzuaWsmL"},"source":["## Overview"]},{"cell_type":"markdown","id":"oQUFQ3wPWyFU","metadata":{"id":"oQUFQ3wPWyFU"},"source":["### Why fine-tune a SLM?\n","\n","Because we want small model for a **specific task** and if we have our own model... we can **run it locally** and everywhere we like (i.e. don't pay API credits or leak our data online).\n","\n","### How to do supervised fine-tuning on an LLM?\n","\n","There are several ways to fine-tune an LLM including reinforcement learning (RL) and Supervised Fine-tuning (SFT).\n","\n","We are going to do SFT because it's the most straightforward.\n","\n","Basically SFT = give samples of inputs and outputs and the model learns to map a given input to a given output.\n","\n","For example if our goal was to extract names:\n","- Input: Hello my name is Daniel\n","- Output: Daniel"]},{"cell_type":"markdown","id":"0c02374e","metadata":{"id":"0c02374e"},"source":["### LLM Fine-tuning Mindset\n","\n","- In LLM world, data inputs are tokens and data outputs are tokens.\n","- A token is a numerical representation of some kind of data.\n","- Computers like numbers (not images, text, videos, etc).\n","- Everything must be turned into numbers.\n","- And data = a very broad term.\n","- It could be text, images, video (series of images), audio, DNA sequences, Excel spreadsheets, you name it.\n","- The goal of the LLM is to be given an input sequence of tokens and then predict the following tokens.\n","\n","So with this mindset, you can think of any problem as **tokens in, tokens out**. Ask yourself: *What tokens do I want to put in and what tokens do I want my model to return?*\n","\n","In our case, we want to put in almost any string input. And we want to get back structured information (mermaid diagrams). This a very specific use case, however, the beauty of LLMs being so general is that you can apply this tokens in, tokens out mindset to almost anything.\n","\n","If you've got an existing dataset (no problem if you don't, you can create one), chances are, you can fine-tune an LLM to do pretty well on it.\n"]},{"cell_type":"markdown","id":"SHfuY921MiOc","metadata":{"id":"SHfuY921MiOc"},"source":["### What we're cooking\n","\n","We're going to build a custom SLM to generate Mermaid sequence diagrams from user prompts.\n","\n","### Ingredients\n","\n","1. Model ([Gemma-3-270M](https://huggingface.co/google/gemma-3-270m-it))\n","2. Datasets\n","  - [Training data](https://huggingface.co/datasets/Celiadraw/text-to-mermaid/viewer/default/train?row=0) (a pre-baked dataset of human-verified examples)\n","  - [Test data](https://huggingface.co/datasets/ibm-research/MermaidSeqBench) (a pre-baked dataset of human-verified examples)\n","3. Training code (Hugging Face Transformers + TRL)\n","4. Eval code\n","5. Demo\n","\n","### Method\n","\n","1. Download model - Hugging Face [`transformers`](https://huggingface.co/docs/transformers/index)\n","2. Download datasets - Hugging Face [`datasets`](https://huggingface.co/docs/datasets/en/index)\n","3. Inspect datasets - Hugging Face `datasets`\n","4. Train model on trainung set - Hugging Face [`trl`](https://huggingface.co/docs/trl/en/index) (TRL = Transformers Reinforcement Learning)\n","5. Eval model - basically just look at a bunch of samples from the test set\n","6. Create an interactive demo - Hugging Face `gradio`\n","7. Bonus: Make the demo public so other people can use it - Hugging Face Spaces"]},{"cell_type":"markdown","id":"kee53jbEMfUc","metadata":{"id":"kee53jbEMfUc"},"source":["### Fine-tuning LLM vs RAG\n","\n","- Fine-tuning &rarr; To do a very specific task, e.g. structured data extraction.\n","- RAG &rarr; You want to inject custom knowledge into an LLM.\n","\n","### Why fine-tune your own model?\n","\n","1. Own the model, can run on own hardware\n","2. Our task is simple enough to just use a small language model\n","3. No API calls needed\n","4. Can run in batch mode to get much faster inference than API calls\n","5. Model by default wasn't very good at our task but now since fine-tuning, is *very good*"]},{"cell_type":"markdown","id":"LmtNj8zHMZuX","metadata":{"id":"LmtNj8zHMZuX"},"source":["### Definitions\n","\n","Some quick definitions of what we're doing.\n","\n","- **Full fine-tuning** - All weights on the model are updated. Often takes longer and requires larger hardware capacity, however, if your model is small enough (e.g. 270M parameter or less), you can often do full fine-tuning.\n","- **LoRA fine-tuning** (also known as partial fine-tuning) - [Low Rank Adaptation](https://arxiv.org/abs/2106.09685) or training a small adapter to attach to your original model. Requires significantly less resources but can perform on par with full fine-tuning. Check this [article](https://thinkingmachines.ai/blog/lora/) on LoRA's pros.\n","- **SLM (Small Language Model)** - A subjective definition but to me a Small Language Model is a model with under 1B parameters, with added bonus for being under 500M parameters. Less parameters generally means less performance. However, when you have a specific task, SLMs often shine because they can be tailored for that specific task."]},{"cell_type":"markdown","id":"692c2190","metadata":{"id":"692c2190"},"source":["## Import dependencies"]},{"cell_type":"code","execution_count":2,"id":"Y1l1SbUTNtJM","metadata":{"executionInfo":{"elapsed":14681,"status":"ok","timestamp":1769700074006,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"Y1l1SbUTNtJM","colab":{"base_uri":"https://localhost:8080/"},"outputId":"106c1465-5950-4260-a6a3-68054f7b15b3"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/532.9 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m532.9/532.9 kB\u001b[0m \u001b[31m15.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h"]}],"source":["# In Google Colab, we need to install `trl`, `accelerate` and `gradio`.\n","\n","!pip install trl accelerate gradio -q"]},{"cell_type":"code","execution_count":3,"id":"421530e1","metadata":{"executionInfo":{"elapsed":31759,"status":"ok","timestamp":1769700105767,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"421530e1"},"outputs":[],"source":["import transformers\n","import trl # trl = Transformers RL Library (https://github.com/huggingface/trl)\n","import datasets\n","import accelerate\n","\n","import gradio as gr"]},{"cell_type":"markdown","id":"8dPVy9IjMDRq","metadata":{"id":"8dPVy9IjMDRq"},"source":["## Setup Environment"]},{"cell_type":"markdown","source":["### GPU\n","This notebook is intended to run in **Google Colab**.\n","\n","Make sure to **enable GPU usage** by going to `Runtime` &rarr; `Change runtime type` &rarr; `GPU T4`"],"metadata":{"id":"7deqJTVnUnAQ"},"id":"7deqJTVnUnAQ"},{"cell_type":"code","execution_count":4,"id":"a0951ae8","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a0951ae8","executionInfo":{"status":"ok","timestamp":1769700106062,"user_tz":180,"elapsed":290,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"}},"outputId":"699986bb-946d-49b4-e9e7-1fe7227ab4b9"},"outputs":[{"output_type":"stream","name":"stdout","text":["GPU: Tesla T4\n","Total Memory:     15828.32 MB | 15.83 GB\n","Allocated Memory: 0.00 MB | 0.00 GB\n","Reserved Memory:  0.00 MB | 0.00 GB\n","Free Memory:      15828.32 MB | 15.83 GB\n"]}],"source":["# Check the amount of GPU memory available (we need at least ~16GB)\n","import torch\n","\n","if torch.cuda.is_available():\n","    device = torch.cuda.current_device()\n","    gpu_name = torch.cuda.get_device_name(device)\n","\n","    total_memory = torch.cuda.get_device_properties(device).total_memory\n","    allocated_memory = torch.cuda.memory_allocated(device)\n","    reserved_memory = torch.cuda.memory_reserved(device)\n","    free_memory = total_memory - reserved_memory\n","\n","    print(f\"GPU: {gpu_name}\")\n","    print(f\"Total Memory:     {total_memory / 1e6:.2f} MB | {total_memory / 1e9:.2f} GB\")\n","    print(f\"Allocated Memory: {allocated_memory / 1e6:.2f} MB | {allocated_memory / 1e9:.2f} GB\")\n","    print(f\"Reserved Memory:  {reserved_memory / 1e6:.2f} MB | {reserved_memory / 1e9:.2f} GB\")\n","    print(f\"Free Memory:      {free_memory / 1e6:.2f} MB | {free_memory / 1e9:.2f} GB\")\n","else:\n","    print(\"No CUDA GPU available\")"]},{"cell_type":"markdown","source":["### Secrets\n","\n","Set your `HF_TOKEN` as env variable by going to `Secrets`."],"metadata":{"id":"CXW7Emt5UI6j"},"id":"CXW7Emt5UI6j"},{"cell_type":"code","source":["# Log in to HF\n","from google.colab import userdata\n","HF_TOKEN = userdata.get('HF_TOKEN')\n","\n","!hf auth login --token $HF_TOKEN\n","!hf auth whoami"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cCljjscLUJ2L","executionInfo":{"status":"ok","timestamp":1769700109614,"user_tz":180,"elapsed":3549,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"}},"outputId":"139ce70f-643c-4bb8-af6d-9246f50e6745"},"id":"cCljjscLUJ2L","execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `hf`CLI if you want to set the git credential as well.\n","Token is valid (permission: write).\n","The token `fine-tune-gemma` has been saved to /root/.cache/huggingface/stored_tokens\n","Your token has been saved to /root/.cache/huggingface/token\n","Login successful.\n","The current active token is: `fine-tune-gemma`\n","\u001b[1muser: \u001b[0m MrObiKenobi\n","\u001b[1morgs: \u001b[0m mcp-course\n"]}]},{"cell_type":"markdown","id":"492095ab","metadata":{"id":"492095ab"},"source":["## Setup Base Model\n","\n","The base model we'll be using is [Gemma 3 270M](https://huggingface.co/google/gemma-3-270m-it/tree/main) from Google.\n","\n","It's the same architecture style as larger LLMs such as Gemini but at a *much* smaller scale. This is why we refer to it as a \"Small Language Model\" or SLM.\n","\n","We can load the model using `transformers`."]},{"cell_type":"code","execution_count":6,"id":"4de92467","metadata":{"executionInfo":{"elapsed":55284,"status":"ok","timestamp":1769700164903,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"4de92467","colab":{"base_uri":"https://localhost:8080/","height":113,"referenced_widgets":["8ba268be6d8a41e3aff69a7bdda6a304","cdecccc878fa4c28ab6be183b3e7744d","9af4db7bd2cc41f98662baa232558d0a","ea63c05737bf414ebf3e306beec6b724","dc6af51e75cb423dbf0c0bd88a9025a9","094b87896991402c91e0ee19ba1ccbbc","50aa5a84e74a4f17916d3c5eeab01562","79e91d2d36a9469a91cff0972eb76450","98dfab1cc9e34e09ae5cc796ba6a49d0","e777397d63f84073a5d31dcb2485798c","726fb8dacbf4479e8045fc59f042f3b2","c128c5027f214b11a68daf89bc0a6916","c6ae49b9816b4310af54060c0103ace5","69057dbdea274d18a23ea50026e8bff6","4fd982ade52945a08ccad93720169a61","606858a45f0b4966bd8721f7b8d42766","38a3f37284c9412187a13734cb41ded2","b4de9ba1ba984de9ba02d0f01f0d57b4","f6eb1f1db13a49af8ab259b1bc646121","5f50c5d5ba8840089c3ba56839fe1ef9","2d50565fadbf4dcb84deddd633a47956","fa3601430d7742258aaa5454cf5fffbc","7bb84eedc19145abaa38859a4521ed2a","79fb1b7bc0f6471fb94e51617854ff54","cd0e4feb10384413bf4288c7d25859f0","0e74a04971ee427bb89fd32bf3046c00","b963de1eab4f4f1bb43f57417f6f610e","d67364efb3134d50958c9e2d021c5061","43264ef5e1794c989a64dee41f7bfd7b","a5614489211244268c84dfbaa8cda1f9","3d94ffd76af34183accd82d97d7075c3","f0ccd9834bcd4600965873367fe0981e","e0b501fb265e4cb1ad7cf0ebf02564dd"]},"outputId":"52b37b71-9cc2-4008-f7a3-ce75d9879c3d"},"outputs":[{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/1.35k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8ba268be6d8a41e3aff69a7bdda6a304"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/536M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c128c5027f214b11a68daf89bc0a6916"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["generation_config.json:   0%|          | 0.00/173 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7bb84eedc19145abaa38859a4521ed2a"}},"metadata":{}}],"source":["from transformers import AutoTokenizer, AutoModelForCausalLM\n","\n","MODEL_NAME = \"google/gemma-3-270m-it\" # \"it\" stands for \"instruction tuned\" which means the model has been tuned for following instructions\n","\n","model = AutoModelForCausalLM.from_pretrained(\n","    MODEL_NAME,\n","    token=HF_TOKEN,\n","    dtype=\"auto\",\n","    device_map=\"auto\", # put the model on the GPU\n","    attn_implementation=\"eager\" # could use flash_attention_2 but ran into issues... so stick with Eager for now\n",")"]},{"cell_type":"code","execution_count":7,"id":"8ae5a6b9","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":244,"referenced_widgets":["7f687ddb98604f028b26b63e7adf0f18","0e6c3a48b4ae408c9cee17721402af30","90ea9314327243508c4fe06e562317af","7ab13a146e2f44f3a414cdd8a8635f41","b2627a947a9c43d0b940b5785869bebe","3f701c31dc8942808a7a55782d5f8990","f1d960f7ca1748b283f55524a8faf19a","613519cff53d4ee4b2aeeb9f80ec833f","0efbd03962dd4589b6a9bfdace60e146","6d5763ec49ca4374ab903a66b51a0faf","eaa73319c9844beab97039aa1c1fd593","e899c22e5ed04abca3e8962ad2d01611","d49f5a4d44be4019a2498f0db9e0c655","87a2f0c6f9084923a1181cc070d5971c","311a9f2d776d474d895366b0c9c41081","95d4d818a37849f3b2bb1f5be5242b6f","9f6df63c71114459a39093b419335a84","ec212317c94d45eebbbd0a935e9142e3","55b318a44b5247ca8753065062b55334","00aec2f9af8944bea3061f11793f8ffc","74b199df1ffb4e85b84cb4d080b9d88b","15d5737e259648619225ece96e308e20","0395e541e8354f718008c83bffb92d23","14c15267fc54498baa7874e94cd91afb","5f770f61daba45b59d74e5d8ad9b1a76","0cb9f0b954f445ac8d9d5e873e90a568","7c28d0683a2b4040abb69b04d1aa81ae","3316ea3a7a384d04863d3de12cc67d65","12571217c4d649f49f1ba2e689576e07","99f9cc38a1284742a8a3f3912ba2e69f","6d533093619c4aa88add75991b25af22","c9440a539b42479ab5d0969606c5c8d3","2670192d96c247478dbf31e97bb434d8","2803cf7e0a974ee2bd19264949e0dc3d","ddffcf72ab57416e80ac97ef895fc067","e33eb1e6cb99453fb4ca9825f636827e","14836b6c85754a64a5cffc7be42e4754","f727ed6348d84334bb10983899d0124f","185417cd3bfb43089dc23a1db4268918","c36a1db945a24e3788da16b409f16015","ca1f491bc1b049d794574501fb8fb22b","b31a8f3ac4cc49099f7039c5ad36ee9e","ca564afa302e4b159b15a35847a57e09","5614ac291fea482aa6d8250402f846f8","e06f669f311b4406a27f96bdadd59129","4b08a5724c8d4352923713a1bfb4bba9","3a7a0bfb5a22477cb46fb539dcda4c45","fbfe679e59a3406a872f168793c01f1a","9dbba63c64bf4031b0f5c8d0d3ab78a7","aafd0b1783a64ba296fc897e292d350a","b1f8b016ca0945fb93fafb09acda2563","771437700f624b28845e542a5df7f96d","20e18eeb76bf4c22b2088ad184de3a2f","519a47b5253842b8a196654e78c80c22","8cae9148cffa45b0bc935554c6e43b58","697fe383468843edafc389db0e585f8e","3cda0ae8259c4adb92f24591d794805c","af6f17d575384fbdaf1b2be5e7d3fdb7","396a3f7d2fe44e13ad361e01d082a487","ffb6e1313d524d13a6c29edca5d74c6e","ef8dca83e21e48f1970a579ea4605c36","c5ddeb31985443ad8f8503c7ea32fde6","381077c050494d8894ba24cb325a44cd","fdc9e4b536dc496986d325792df173b1","edd0860eb7804d5692a8ceff66d876a6","1baf573bc53a4124bfd245339d8efed6"]},"executionInfo":{"elapsed":15346,"status":"ok","timestamp":1769700180251,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"8ae5a6b9","outputId":"bead8390-7314-4deb-c0fd-df96bdfd21c4"},"outputs":[{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/1.16M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7f687ddb98604f028b26b63e7adf0f18"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.model:   0%|          | 0.00/4.69M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e899c22e5ed04abca3e8962ad2d01611"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json:   0%|          | 0.00/33.4M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0395e541e8354f718008c83bffb92d23"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["added_tokens.json:   0%|          | 0.00/35.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2803cf7e0a974ee2bd19264949e0dc3d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["special_tokens_map.json:   0%|          | 0.00/662 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e06f669f311b4406a27f96bdadd59129"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["chat_template.jinja:   0%|          | 0.00/1.53k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"697fe383468843edafc389db0e585f8e"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["[INFO] Model on device: cuda:0\n","[INFO] Model using dtype: torch.bfloat16\n"]}],"source":["# Our model requires numbers (tokens) as input. We can turn strings into tokens via its tokenizer!\n","\n","tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, token=HF_TOKEN)\n","\n","print(f\"[INFO] Model on device: {model.device}\")\n","print(f\"[INFO] Model using dtype: {model.dtype}\")"]},{"cell_type":"code","execution_count":8,"id":"e8d187d0","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1769700180261,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"e8d187d0","outputId":"299bac14-5404-49cb-b721-6e62db827f7a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'input_ids': [2, 9259, 1041, 1463, 563, 13108], 'attention_mask': [1, 1, 1, 1, 1, 1]}"]},"metadata":{},"execution_count":8}],"source":["# Try the tokenizer\n","tokenizer(\"Hello my name is Obi-Wan Kenobi\")"]},{"cell_type":"code","execution_count":9,"id":"5cf6296a","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1531,"status":"ok","timestamp":1769700181794,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"5cf6296a","outputId":"03e45625-dc65-4228-f88c-83b832ee5336"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["odict_keys(['logits', 'past_key_values'])"]},"metadata":{},"execution_count":9}],"source":["import torch\n","\n","# Make inference to get logits\n","outputs = model(torch.tensor(tokenizer(\"Hello my name is Daniel\")[\"input_ids\"]).unsqueeze(0).to(\"cuda\"))\n","outputs.keys()"]},{"cell_type":"markdown","id":"629cb237","metadata":{"id":"629cb237"},"source":["## Get Datasets\n","\n","Our datasets are [Celiadraw/text-to-mermaid](https://huggingface.co/datasets/Celiadraw/text-to-mermaid/viewer/default/train?row=0) and [ibm-research/MermaidSeqBench](https://huggingface.co/datasets/ibm-research/MermaidSeqBench). We will be using the Celiadraw dataset to train and validate our model... and later the IBM dataset for testing.\n","\n","The main thing we are concerned about is that we want the input to our model to be the `\"prompt\"` column and the output to be the `\"output\"` column. We'll explore these below."]},{"cell_type":"code","execution_count":10,"id":"5721a3b1","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":130,"referenced_widgets":["a8cee367b73341999616f7ee257f78f9","3a3f45d43e4544a082fa9c64ca16c1da","b55704dd697941ff9e4217ddec430647","014a5dfa7ebf41318b0b11f49c18a0ad","c5c1c600aa6e4ee785bc2e7b737eeaa6","6409b3b294aa4ede97f0836c2bd50034","a889c42b89b846e3bf9128f872d8736e","49d05d39d97e442a81a4a849952af21f","4591863e12ba47809d463fee2b997638","c13951aa994f47769f61cb5cb7f49bb2","cfece3cf78ea49e9914cfdaedc640469","51615ebb5d5d441abc38cf515928537e","3bc4732f11934066b25860747b9cb411","dc0843fc605e49eab87d9df1663463f6","caa21f035863457ba5618bd2eef0f0f2","bd678d6e56f6418f99c72f070479c3c9","5ce34526819641c689d0c8c1dc57bb0b","51596f2c778d41c9a6dd067d926e1f7f","bf05d4bb6c2040b6838e170393caa713","9cd39d8b9ca84bbb907801f2b6f85e37","429dcba16f1a4458a3bf0e3cb57da115","28c269300bb748fcb912ff1a80b61a6c","6b207e38a9eb446394b2f8ba91a756f6","ca0cd6144da2455185f6d15086249ab5","efb72a6c55644e89b73072627736c462","1ede6878f257471fb39b89b29dbea2f9","07d7eddb398040978f92dbebb798e75b","2a269d7849fc48c2bc8cc0d0bf37a548","0999bdaa03ab4207a81e551b14203db3","38d974525beb4e48a94a2ef6538af8fe","c7d87bde55cf4ec6b07b119b5ce052ba","cac0a90879c54f3da809b8720516201a","b599bbe362f44656969937ff57818243"]},"executionInfo":{"elapsed":11456,"status":"ok","timestamp":1769700193251,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"5721a3b1","outputId":"5f285970-ecd7-4f2b-c7b3-5b95353431d6"},"outputs":[{"output_type":"display_data","data":{"text/plain":["README.md:   0%|          | 0.00/116 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a8cee367b73341999616f7ee257f78f9"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["true_clean.csv: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"51615ebb5d5d441abc38cf515928537e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split:   0%|          | 0/9748 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6b207e38a9eb446394b2f8ba91a756f6"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["[INFO] Number of samples in the dataset: 9748\n"]}],"source":["from datasets import load_dataset\n","\n","dataset = load_dataset(\"Celiadraw/text-to-mermaid\")\n","\n","print(f\"[INFO] Number of samples in the dataset: {len(dataset['train'])}\")"]},{"cell_type":"code","execution_count":11,"id":"eGKyAKweaXRi","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":86,"status":"ok","timestamp":1769700193338,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"eGKyAKweaXRi","outputId":"1394f2a0-fe82-47d1-e3f8-f881db705aa8"},"outputs":[{"output_type":"stream","name":"stdout","text":["DatasetDict({\n","    train: Dataset({\n","        features: ['input', 'output'],\n","        num_rows: 1000\n","    })\n","})\n"]}],"source":["# Normalize dataset column names\n","dataset = dataset.rename_columns({\n","    \"prompt\": \"input\",\n","    \"output\": \"output\",\n","})\n","\n","# [OPTIONAL] HW is limited, so we are just going to use a 1k-sample subset\n","dataset[\"train\"] = (\n","    dataset[\"train\"]\n","    .shuffle(seed=42)\n","    .select(range(1000))\n",")\n","\n","print(dataset)"]},{"cell_type":"code","execution_count":12,"id":"3fa6954c","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1769700193352,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"3fa6954c","outputId":"e979276c-f912-4e99-ce23-2c8be96dcb33"},"outputs":[{"output_type":"stream","name":"stdout","text":["[INFO] Input:\n","Design a sequence diagram for a video conferencing application, illustrating interactions between users, scheduling system, video call establishment, audio transmission, and chat messaging.\n","\n","[INFO] Example structured output:\n","sequenceDiagram\n","  participant User1\n","  participant User2\n","  participant SchedulingSystem as Scheduling System\n","  participant VideoCall as Video Call\n","  participant AudioTransmission as Audio Transmission\n","  participant ChatMessaging as Chat Messaging\n","  User1 ->> SchedulingSystem: Schedule Meeting\n","  SchedulingSystem ->> User2: Meeting Invitation\n","  User1 ->> VideoCall: Start Call\n","  VideoCall ->> User2: Receive Call\n","  VideoCall ->> AudioTransmission: Transmit Audio\n","  VideoCall ->> ChatMessaging: Send Message\n","  ChatMessaging -->> User2: Receive Message\n"]}],"source":["import json\n","import random\n","\n","def get_random_idx(dataset):\n","    \"\"\"Returns a random integer index based on the number of samples in the dataset.\"\"\"\n","    random_idx = random.randint(0, len(dataset)-1)\n","    return random_idx\n","\n","\n","random_idx = get_random_idx(dataset[\"train\"])\n","random_sample = dataset[\"train\"][random_idx]\n","\n","example_input = random_sample[\"input\"]\n","example_output = random_sample[\"output\"]\n","\n","print(f\"[INFO] Input:\\n{example_input}\") # what goes in\n","print()\n","print(f\"[INFO] Example structured output:\\n{example_output}\") # what we want our model to learn to predict"]},{"cell_type":"markdown","id":"9e80cff0","metadata":{"id":"9e80cff0"},"source":["### Format the dataset into LLM-style inputs/outputs\n","\n","Right now we have examples of string-based inputs and structured outputs.\n","\n","However, our LLMs generally want things in the format of:\n","\n","```\n","{\n","  \"user\": \"Hello my name is Daniel\",\n","  \"system\": \"Hi Daniel, I'm an LLM\"\n","}\n","```\n","\n","In other words, they want structure around the intputs and outputs rather than just raw information.\n","\n","> See the dataset formats and types in the [TRL docs](https://huggingface.co/docs/trl/en/dataset_formats)"]},{"cell_type":"code","execution_count":13,"id":"6d909b36","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":110,"status":"ok","timestamp":1769700193463,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"6d909b36","outputId":"0b0f6ee8-6dbd-41ed-c117-66b36851fe4a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'input': 'Design a sequence diagram for a video conferencing application, illustrating interactions between users, scheduling system, video call establishment, audio transmission, and chat messaging.',\n"," 'output': 'sequenceDiagram\\n  participant User1\\n  participant User2\\n  participant SchedulingSystem as Scheduling System\\n  participant VideoCall as Video Call\\n  participant AudioTransmission as Audio Transmission\\n  participant ChatMessaging as Chat Messaging\\n  User1 ->> SchedulingSystem: Schedule Meeting\\n  SchedulingSystem ->> User2: Meeting Invitation\\n  User1 ->> VideoCall: Start Call\\n  VideoCall ->> User2: Receive Call\\n  VideoCall ->> AudioTransmission: Transmit Audio\\n  VideoCall ->> ChatMessaging: Send Message\\n  ChatMessaging -->> User2: Receive Message'}"]},"metadata":{},"execution_count":13}],"source":["# This is the raw data:\n","random_sample"]},{"cell_type":"code","execution_count":14,"id":"16769fa3","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1769700193469,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"16769fa3","outputId":"e353ae0c-9fab-44b9-f043-e2b6422c7a34"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'messages': [{'role': 'user',\n","   'content': 'Design a sequence diagram for a video conferencing application, illustrating interactions between users, scheduling system, video call establishment, audio transmission, and chat messaging.'},\n","  {'role': 'system',\n","   'content': 'sequenceDiagram\\n  participant User1\\n  participant User2\\n  participant SchedulingSystem as Scheduling System\\n  participant VideoCall as Video Call\\n  participant AudioTransmission as Audio Transmission\\n  participant ChatMessaging as Chat Messaging\\n  User1 ->> SchedulingSystem: Schedule Meeting\\n  SchedulingSystem ->> User2: Meeting Invitation\\n  User1 ->> VideoCall: Start Call\\n  VideoCall ->> User2: Receive Call\\n  VideoCall ->> AudioTransmission: Transmit Audio\\n  VideoCall ->> ChatMessaging: Send Message\\n  ChatMessaging -->> User2: Receive Message'}]}"]},"metadata":{},"execution_count":14}],"source":["def sample_to_conversation(sample):\n","    \"\"\"Helper function to convert an input sample to conversation style.\"\"\"\n","    return {\n","        \"messages\": [\n","            {\"role\": \"user\", \"content\": sample[\"input\"]}, # Load the prompt from the dataset\n","            {\"role\": \"system\", \"content\": sample[\"output\"]} # Load the mermaid diagram target\n","        ]\n","    }\n","\n","# Notice how we now have a `\"messages\"` key in our dataset samples...\n","sample_to_conversation(random_sample)"]},{"cell_type":"code","execution_count":15,"id":"09cca9d2","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["7173151219c440adadbb21ec6cefbb7f","8f11ed1b97c349cba696816428b571f6","b798ff9d8b444d0fbf818f5c863a44e8","e88e8b97c27649319d7067e42f8e1c72","e74f671ed64540a3b9d3fc6387d1776a","77209066e41646f4a7d6f75c585bd06d","7b6a9117c8624f529aadda5ece126894","8a492e48d85e4cd2bd87396e460c7f8d","f67029dcd57d47a0962824dabe30dbe2","9636ffb8022d48aa89da57a702e6313b","497dc8491910426b9a47c389a3ace570"]},"executionInfo":{"elapsed":311,"status":"ok","timestamp":1769700193781,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"09cca9d2","outputId":"f384a1d9-02c8-4e54-acda-0d2cbdbd705d"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/1000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7173151219c440adadbb21ec6cefbb7f"}},"metadata":{}}],"source":["# Map our sample_to_conversation function to dataset\n","dataset = dataset.map(sample_to_conversation,\n","                      batched=False)"]},{"cell_type":"code","execution_count":16,"id":"f027190f","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1769700193793,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"f027190f","outputId":"ab2db0fe-11b9-4de5-8e51-7d9818656536"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['input', 'output', 'messages'],\n","        num_rows: 800\n","    })\n","    test: Dataset({\n","        features: ['input', 'output', 'messages'],\n","        num_rows: 200\n","    })\n","})"]},"metadata":{},"execution_count":16}],"source":["# Create a train/test split\n","dataset = dataset[\"train\"].train_test_split(test_size=0.2,\n","                                            shuffle=False,\n","                                            seed=42)\n","\n","# Number #1 rule in machine learning\n","# Always train on the train set and test on the test set\n","# This gives us an indication of how our model will perform in the real world\n","\n","dataset"]},{"cell_type":"markdown","source":["## Try Base Model"],"metadata":{"id":"jR5ILDA1XeNH"},"id":"jR5ILDA1XeNH"},{"cell_type":"markdown","id":"84196f5b","metadata":{"id":"84196f5b"},"source":["### Try the model with a pipeline"]},{"cell_type":"code","execution_count":17,"id":"utdewtXBaPJA","metadata":{"executionInfo":{"elapsed":24,"status":"ok","timestamp":1769700193853,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"utdewtXBaPJA"},"outputs":[],"source":["def create_easy_sample(input):\n","    template = {\"role\": \"user\", \"content\": input}\n","    return template\n","\n","input_text = \"\"\"\n","Develop a timing diagram for a traffic light control system.\n","Include signals such as Green Light, Yellow Light, Red Light, and illustrate the timing intervals for each signal.\n","\"\"\"\n","\n","easy_sample = create_easy_sample(input=input_text)"]},{"cell_type":"code","execution_count":18,"id":"1a9871a6","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":58648,"status":"ok","timestamp":1769700252503,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"1a9871a6","outputId":"564f0411-6df5-4828-ab22-7e21b689bd7e"},"outputs":[{"output_type":"stream","name":"stderr","text":["Device set to use cuda:0\n"]},{"output_type":"stream","name":"stdout","text":["[INFO] This is the input prompt: <bos><start_of_turn>user\n","Develop a timing diagram for a traffic light control system.\n","Include signals such as Green Light, Yellow Light, Red Light, and illustrate the timing intervals for each signal.<end_of_turn>\n","<start_of_turn>model\n","\n","\n","[INFO] Input:\n","\n","Develop a timing diagram for a traffic light control system.\n","Include signals such as Green Light, Yellow Light, Red Light, and illustrate the timing intervals for each signal.\n","\n","\n","[INFO] Output from google/gemma-3-270m-it:\n","\n","Okay, let's create a timing diagram for a traffic light control system. Here's a breakdown of the components, their timing, and how they work.\n","\n","**Traffic Light Control System Timing Diagram**\n","\n","**Components:**\n","\n","*   **Light Sensor:** Detects the incoming traffic light signal.\n","*   **Signal Generator:** Generates the traffic light signal.\n","*   **Control Unit:** Manages the signal generation and control.\n","*   **Control Buttons:** Control the signal generation and control.\n","*   **Control Interface:** Provides the user with the signal control.\n","*   **Power Supply:** Provides the necessary power for the system.\n","*   **Grounding:** Ensures the system is properly grounded.\n","*   **Safety Features:** Includes safety measures to prevent electrical hazards.\n","\n","**Diagram:**\n","\n","```\n","+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n"]}],"source":["from transformers import pipeline\n","\n","# Load model and use it as a pipeline\n","pipe = pipeline(\"text-generation\",\n","                model=model,\n","                tokenizer=tokenizer)\n","\n","input_prompt = pipe.tokenizer.apply_chat_template([easy_sample], # pipeline tokenizer wants a list of inputs\n","                                                  tokenize=False,\n","                                                  add_generation_prompt=True)\n","\n","print(f\"[INFO] This is the input prompt: {input_prompt}\")\n","print()\n","\n","default_outputs = pipe(input_prompt,\n","                       max_new_tokens=512,\n","                       disable_compile=True)\n","\n","print(f\"[INFO] Input:\\n{input_text}\")\n","print()\n","print(f\"[INFO] Output from {MODEL_NAME}:\")\n","print()\n","print(default_outputs[0][\"generated_text\"][len(input_prompt):])"]},{"cell_type":"markdown","id":"1536cc33","metadata":{"id":"1536cc33"},"source":["### Try the model on one dataset example"]},{"cell_type":"code","execution_count":19,"id":"39394d8b","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":29227,"status":"ok","timestamp":1769700281729,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"39394d8b","outputId":"00bab742-978d-46bf-bebf-43f58e9f9722"},"outputs":[{"output_type":"stream","name":"stdout","text":["[INFO] Input:\n","<bos><start_of_turn>user\n","Illustrate an activity diagram for an online course registration system. Include activities such as browse courses, enroll in course, pay course fee, access course materials, and complete course.<end_of_turn>\n","<start_of_turn>model\n","\n","\n","[INFO] Output:\n","Here's an illustration of an online course registration system with activities, including browsing, enrollment, payment, and completion:\n","\n","**Course Registration System - Online Learning Platform**\n","\n","**Goal:** To provide a user-friendly and efficient platform for students to register for online courses and manage their enrolled courses.\n","\n","**Key Features:**\n","\n","*   **User Registration:**\n","    *   Secure registration form with fields for student name, email, username, and password.\n","    *   Option to select a course.\n","    *   Ability to add a course to their profile.\n","    *   Option to view course details (credits, duration, prerequisites, instructor, etc.).\n","    *   Option to view course details and track progress.\n","    *   Option to track course completion.\n","    *   Option to manage course categories and tags.\n","    *   Option to upload a profile picture.\n","    *   Option to set a learning schedule.\n","    *   Option to set a payment method.\n","    *   Option to view course testimonials and ratings.\n","    *   Option to share course progress on social media.\n","\n","**Activities:**\n","\n","1.  **Browse Courses:**\n","    *   A search bar with filters for course title, description, instructor\n"]}],"source":["# Get a random sample\n","random_idx = get_random_idx(dataset[\"train\"])\n","random_train_sample = dataset[\"train\"][random_idx]\n","\n","# Apply the chat template\n","input_prompt = pipe.tokenizer.apply_chat_template(conversation=random_train_sample[\"messages\"][:1],\n","                                                  tokenize=False,\n","                                                  add_generation_prompt=True)\n","\n","# Let's run the default model on our input\n","default_outputs = pipe(text_inputs=input_prompt, max_new_tokens=256)\n","\n","# View and compare the outputs\n","print(f\"[INFO] Input:\\n{input_prompt}\\n\")\n","print(f\"[INFO] Output:\\n{default_outputs[0]['generated_text'][len(input_prompt):]}\")"]},{"cell_type":"markdown","id":"f9f4775f-ec4b-4e1d-875c-5e33213bde71","metadata":{"id":"f9f4775f-ec4b-4e1d-875c-5e33213bde71"},"source":["By default **the model produces a fairly generic response**... This is expected and good. It means the model has a good baseline understanding of language.\n","\n","If it responded with pure garbage, we might have an uphill battle. However, this response type is not what we want. We want our model to respond with structured data based on the input.\n","\n","Good news is we can adjust the patterns in our model to do just that."]},{"cell_type":"markdown","id":"7b70eec2","metadata":{"id":"7b70eec2"},"source":["### Let's try to prompt the model\n","\n","We want a model to generate mermaid diagrams based on user instructions text. By default the model will just reply to any text input with a generic response... However, we can try and get our ideal outputs via **prompting**."]},{"cell_type":"code","execution_count":20,"id":"a1f93224","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1769700281736,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"a1f93224","outputId":"6f0c5168-d966-481b-d7e9-b73b31c1be6a"},"outputs":[{"output_type":"stream","name":"stdout","text":["[INFO] Original content:\n","Illustrate an activity diagram for an online course registration system. Include activities such as browse courses, enroll in course, pay course fee, access course materials, and complete course.\n","\n","[INFO] New content with instructions in prompt:\n","\n","Generate a valid, syntactically correct MermaidJS UML sequence diagram based on the provided structured INPUT.\n","\n","Input Format:\n","- Purpose: Description of the diagram\n","- Main Components: List of involved components\n","- Interactions: Sequence of interactions\n","\n","General Guidelines:\n","- Identify interactions between the Main Components, distinguishing requests (->>) and responses (-->>).\n","- Structure participants in this order: Users -> Frontend (Mobile/Web) -> Backend (BFF, Middleware, SOR, DB).\n","- Determine all the alternate cases needed, if-else, alt, and make sure they are all covered.\n","- Make sure there is a response to every request.\n","- Format API calls as REST API (e.g., `GET /Workorder(params)`, `POST /Workorder(params)`).\n","- Handle alternate flows (if, if-else, nested conditions) per MermaidJS syntax.\n","- Ensure syntax correctness, covering all responses, error scenarios, and system failures.\n","- Use activation (`activate` actor) and deactivation (`deactivate` actor) properly.\n","- Add notes where applicable.\n","\n","MermaidJS Syntax Rules:\n","- Begin with 'sequenceDiagram'.\n","- Use 'participant ActorName' to introduce actors.\n","- Activate ('activate') actors when they receive messages; deactivate ('deactivate') after responding.\n","- Treat alternate case like \"if{}\", \"if{} else {}\", nested if statements has to be handled.\n","- Ensure that any actor activated before an alternate flow must deactivate after it ends.\n","- Follow structured handling of conditional cases (see Example).\n","- Add notes over the actor only when required. Notes should address the question of \"why\". Notes can also be used to segregate groups of request/response flows. Example: `Note over Actor: notes content`\n","- Respond only with the MermaidJS code, formatted as markdown. Do not include explanations or additional text.\n","\n","Example Input:\n","Purpose: Client-Server Request Handling\n","Main Components: Client (CT), Server (SR)\n","Interactions:\n","Client Action: The client sends a request with data (X) to the server.\n","Server Processing:\n","- The server receives the request and processes it.\n","- If the request is valid, the server sends a successful response with data (Y).\n","- If the request is invalid, the server returns an error message indicating invalid data.\n","Response:\n","- Upon successful processing, the client receives the requested data.\n","- If an error occurs, the client is notified accordingly.\n","\n","Example Output:\n","```sequenceDiagram\n","participant CT as Client\n","participant SR as Server\n","Note over CT: Sending a request\n","CT->>SR: Request (Data: X)\n","activate SR\n","alt Successful Response\n","SR-->>CT: Response (Data: Y)\n","else Error Case\n","SR-->>CT: Error (Invalid Data)\n","end\n","deactivate SR```\n","\n","INPUT:\n","Illustrate an activity diagram for an online course registration system. Include activities such as browse courses, enroll in course, pay course fee, access course materials, and complete course.\n","\n"]}],"source":["prompt_instruction = \"\"\"\n","Generate a valid, syntactically correct MermaidJS UML sequence diagram based on the provided structured INPUT.\n","\n","Input Format:\n","- Purpose: Description of the diagram\n","- Main Components: List of involved components\n","- Interactions: Sequence of interactions\n","\n","General Guidelines:\n","- Identify interactions between the Main Components, distinguishing requests (->>) and responses (-->>).\n","- Structure participants in this order: Users -> Frontend (Mobile/Web) -> Backend (BFF, Middleware, SOR, DB).\n","- Determine all the alternate cases needed, if-else, alt, and make sure they are all covered.\n","- Make sure there is a response to every request.\n","- Format API calls as REST API (e.g., `GET /Workorder(params)`, `POST /Workorder(params)`).\n","- Handle alternate flows (if, if-else, nested conditions) per MermaidJS syntax.\n","- Ensure syntax correctness, covering all responses, error scenarios, and system failures.\n","- Use activation (`activate` actor) and deactivation (`deactivate` actor) properly.\n","- Add notes where applicable.\n","\n","MermaidJS Syntax Rules:\n","- Begin with 'sequenceDiagram'.\n","- Use 'participant ActorName' to introduce actors.\n","- Activate ('activate') actors when they receive messages; deactivate ('deactivate') after responding.\n","- Treat alternate case like \"if{}\", \"if{} else {}\", nested if statements has to be handled.\n","- Ensure that any actor activated before an alternate flow must deactivate after it ends.\n","- Follow structured handling of conditional cases (see Example).\n","- Add notes over the actor only when required. Notes should address the question of \"why\". Notes can also be used to segregate groups of request/response flows. Example: `Note over Actor: notes content`\n","- Respond only with the MermaidJS code, formatted as markdown. Do not include explanations or additional text.\n","\n","Example Input:\n","Purpose: Client-Server Request Handling\n","Main Components: Client (CT), Server (SR)\n","Interactions:\n","Client Action: The client sends a request with data (X) to the server.\n","Server Processing:\n","- The server receives the request and processes it.\n","- If the request is valid, the server sends a successful response with data (Y).\n","- If the request is invalid, the server returns an error message indicating invalid data.\n","Response:\n","- Upon successful processing, the client receives the requested data.\n","- If an error occurs, the client is notified accordingly.\n","\n","Example Output:\n","```sequenceDiagram\n","participant CT as Client\n","participant SR as Server\n","Note over CT: Sending a request\n","CT->>SR: Request (Data: X)\n","activate SR\n","alt Successful Response\n","SR-->>CT: Response (Data: Y)\n","else Error Case\n","SR-->>CT: Error (Invalid Data)\n","end\n","deactivate SR```\n","\n","INPUT:\n","<targ_input_text>\n","\"\"\"\n","\n","def update_input_message_content(input):\n","    original_content = input[\"messages\"][:1][0][\"content\"]\n","    new_content = prompt_instruction.replace(\"<targ_input_text>\", original_content)\n","\n","    new_input = [{\"content\": new_content,\n","                  \"role\": \"user\"}]\n","\n","    return new_input\n","\n","print(f'[INFO] Original content:\\n{random_train_sample[\"messages\"][:1][0][\"content\"]}')\n","print()\n","print(f'[INFO] New content with instructions in prompt:')\n","print(update_input_message_content(input=random_train_sample)[0][\"content\"])"]},{"cell_type":"code","execution_count":21,"id":"22ed0f84","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":27230,"status":"ok","timestamp":1769700308967,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"22ed0f84","outputId":"72fcec49-796a-450c-e595-035b2d4ba807"},"outputs":[{"output_type":"stream","name":"stdout","text":["[INFO] Input:\n","<bos><start_of_turn>user\n","Generate a valid, syntactically correct MermaidJS UML sequence diagram based on the provided structured INPUT.\n","\n","Input Format:\n","- Purpose: Description of the diagram\n","- Main Components: List of involved components\n","- Interactions: Sequence of interactions\n","\n","General Guidelines:\n","- Identify interactions between the Main Components, distinguishing requests (->>) and responses (-->>).\n","- Structure participants in this order: Users -> Frontend (Mobile/Web) -> Backend (BFF, Middleware, SOR, DB).\n","- Determine all the alternate cases needed, if-else, alt, and make sure they are all covered.\n","- Make sure there is a response to every request.\n","- Format API calls as REST API (e.g., `GET /Workorder(params)`, `POST /Workorder(params)`).\n","- Handle alternate flows (if, if-else, nested conditions) per MermaidJS syntax.\n","- Ensure syntax correctness, covering all responses, error scenarios, and system failures.\n","- Use activation (`activate` actor) and deactivation (`deactivate` actor) properly.\n","- Add notes where applicable.\n","\n","MermaidJS Syntax Rules:\n","- Begin with 'sequenceDiagram'.\n","- Use 'participant ActorName' to introduce actors.\n","- Activate ('activate') actors when they receive messages; deactivate ('deactivate') after responding.\n","- Treat alternate case like \"if{}\", \"if{} else {}\", nested if statements has to be handled.\n","- Ensure that any actor activated before an alternate flow must deactivate after it ends.\n","- Follow structured handling of conditional cases (see Example).\n","- Add notes over the actor only when required. Notes should address the question of \"why\". Notes can also be used to segregate groups of request/response flows. Example: `Note over Actor: notes content`\n","- Respond only with the MermaidJS code, formatted as markdown. Do not include explanations or additional text.\n","\n","Example Input:\n","Purpose: Client-Server Request Handling\n","Main Components: Client (CT), Server (SR)\n","Interactions:\n","Client Action: The client sends a request with data (X) to the server.\n","Server Processing:\n","- The server receives the request and processes it.\n","- If the request is valid, the server sends a successful response with data (Y).\n","- If the request is invalid, the server returns an error message indicating invalid data.\n","Response:\n","- Upon successful processing, the client receives the requested data.\n","- If an error occurs, the client is notified accordingly.\n","\n","Example Output:\n","```sequenceDiagram\n","participant CT as Client\n","participant SR as Server\n","Note over CT: Sending a request\n","CT->>SR: Request (Data: X)\n","activate SR\n","alt Successful Response\n","SR-->>CT: Response (Data: Y)\n","else Error Case\n","SR-->>CT: Error (Invalid Data)\n","end\n","deactivate SR```\n","\n","INPUT:\n","Illustrate an activity diagram for an online course registration system. Include activities such as browse courses, enroll in course, pay course fee, access course materials, and complete course.<end_of_turn>\n","<start_of_turn>model\n","\n","\n","[INFO] Output:\n","```mermaid\n","workflow\n","    ACTOR ->  Client Action\n","    ACTOR ->  Server Processing\n","    ACTOR ->  Server Processing\n","    ACTOR ->  Server Processing\n","    ACTOR ->  Server Processing\n","    ACTOR ->  Client Action\n","    ACTOR ->  Server Processing\n","    ACTOR ->  Client Action\n","    ACTOR ->  Server Processing\n","    ACTOR ->  Client Action\n","    ACTOR ->  Server Processing\n","    ACTOR ->  Client Action\n","    ACTOR ->  Server Processing\n","    ACTOR ->  Client Action\n","    ACTOR ->  Server Processing\n","    ACTOR ->  Client Action\n","    ACTOR ->  Server Processing\n","    ACTOR ->  Client Action\n","    ACTOR ->  Server Processing\n","    ACTOR ->  Client Action\n","    ACTOR ->  Server Processing\n","    ACTOR ->  Client Action\n","    ACTOR ->  Server Processing\n","    ACTOR ->  Client Action\n","    ACTOR ->  Server Processing\n","    ACTOR ->  Client Action\n","    ACTOR ->  Server Processing\n","    ACTOR ->  Client Action\n","    ACTOR ->  Server Processing\n","    ACTOR ->  Client Action\n","    ACTOR ->  Server Processing\n","    ACTOR\n"]}],"source":["# Apply the chat template\n","updated_input_prompt = update_input_message_content(input=random_train_sample)\n","\n","input_prompt = pipe.tokenizer.apply_chat_template(conversation=updated_input_prompt,\n","                                                  tokenize=False,\n","                                                  add_generation_prompt=True)\n","\n","# Let's run the default model on our input\n","default_outputs = pipe(text_inputs=input_prompt,\n","                       max_new_tokens=256)\n","\n","# View and compare the outputs\n","print(f\"[INFO] Input:\\n{input_prompt}\\n\")\n","print(f\"[INFO] Output:\\n{default_outputs[0]['generated_text'][len(input_prompt):]}\")"]},{"cell_type":"code","execution_count":22,"id":"ff2b1392","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1769700308978,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"ff2b1392","outputId":"1d091369-7fee-432f-857c-3099c82ee2fe"},"outputs":[{"output_type":"stream","name":"stdout","text":["Illustrate an activity diagram for an online course registration system. Include activities such as browse courses, enroll in course, pay course fee, access course materials, and complete course.\n","\n","graph TD;\n","    subgraph Online Course Registration System;\n","        Browse_Courses --> Enroll_in_Course;\n","        Enroll_in_Course --> Pay_Course_Fee;\n","        Pay_Course_Fee --> Access_Course_Materials;\n","        Access_Course_Materials --> Complete_Course;\n","    end;\n"]}],"source":["# This is our input:\n","print(random_train_sample[\"messages\"][0][\"content\"])\n","print()\n","\n","# This is our ideal output:\n","print(random_train_sample[\"messages\"][1][\"content\"])"]},{"cell_type":"markdown","id":"3fe2e159","metadata":{"id":"3fe2e159"},"source":["Okay looks like the foundation **SLM doesn't do what we want it to do**... No matter, we can fine-tune it so it does our specific task!"]},{"cell_type":"markdown","id":"bf6784ab","metadata":{"id":"bf6784ab"},"source":["## Fine-tuning our model\n","\n","1. Setup [SFTConfig](https://huggingface.co/docs/trl/v0.26.2/en/sft_trainer#trl.SFTConfig) (Supervised Fine-tuning Config)\n","\n","2. Use [SFTTrainer](https://huggingface.co/docs/trl/v0.26.2/en/sft_trainer#trl.SFTTrainer) (Supervised Fine-tuning Trainer) to train our model on our supervised samples"]},{"cell_type":"code","execution_count":23,"id":"c5f29e96","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":10737,"status":"ok","timestamp":1769700319717,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"c5f29e96","outputId":"1596735e-d93a-46d7-8a98-8a076ea716e6"},"outputs":[{"output_type":"stream","name":"stdout","text":["[INFO] Using dtype: torch.bfloat16\n","[INFO] Using learning rate: 5e-05\n"]}],"source":["# Setting up our SFTConfig\n","from trl import SFTConfig\n","\n","torch_dtype = model.dtype\n","\n","CHECKPOINT_DIR_NAME = \"./checkpoint_models\"\n","BASE_LEARNING_RATE = 5e-5\n","\n","print(f\"[INFO] Using dtype: {torch_dtype}\")\n","print(f\"[INFO] Using learning rate: {BASE_LEARNING_RATE}\")\n","\n","sft_config = SFTConfig(\n","    output_dir=CHECKPOINT_DIR_NAME,\n","    max_length=512,\n","    packing=False,\n","    num_train_epochs=3,\n","    per_device_train_batch_size=4, # Note: you can change this depending on the amount of VRAM your GPU has\n","    per_device_eval_batch_size=4,\n","    gradient_checkpointing=False,\n","    optim=\"adamw_torch_fused\", # Note: if you try \"adamw\", you will get an error\n","    logging_steps=1,\n","    save_strategy=\"epoch\",\n","    eval_strategy=\"epoch\",\n","    learning_rate=BASE_LEARNING_RATE,\n","    fp16=True if torch_dtype == torch.float16 else False,\n","    bf16=True if torch_dtype == torch.float16 else False,\n","    lr_scheduler_type=\"constant\",\n","    push_to_hub=False,\n","    report_to=None\n",")\n","\n","# There are a lot of settings in the sft_config, so feel free to uncomment this and inspect it if you want\n","#sft_config\n","\n","# Config setup, now we can train our model with `SFTTrainer`!"]},{"cell_type":"code","execution_count":24,"id":"a6a74f51","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":751,"referenced_widgets":["80d6b89f9fda42d9ba93264482b7dc0b","50e64dbafbc24c7aa8c6f7520f9dbd2f","2b95cfe5939c43e1b9962090da882371","97030877674f4cabab4ba7f06570da87","ac225537ea3c473793e433afcf5f13d0","df9f9049f26944d490358626fda2901b","1a9d262e2a034a4d802ca0c2ef7b293a","42f3f9751f2345c58430f22f9b1c5555","6e090296b67b41348239fc5e8816cce3","876ce9955cd0444b96ab0ef2266be2cc","8760414d9015457fbbf4e1d7aaa90c96","8a6aed98f0ff4a3caf0708ccd45d8fd6","5298151712424b4da9d0e72a25c91c4e","6cde4876543241bcbf23a844e49dad61","8f01db768bb043789267622e4ae91410","5e45a8c3e5424caeb48a11dfc2f02b3c","458fed5b6bc8482985909d5a74104372","a052dfaa236a46748322c734b806a716","f09157a38a8d4bee9b59c7451c09ed7a","076d9faa82cc4c069dd44dfbba7f5d53","45aca92063ad416b9d6417180e4ccf96","afe81a0a91f34475b7a8eb6a4a713405","44fde7cbae52407faff7341a35412503","6b55b3f01b4b464e8408f160fe33216e","666e2c1b78dc4f46ba6a0ee4ea3e01c5","09c3db72ee2945228fbf76cc7c1324e4","60553e58a9e64be3af8422640f925c71","d16f6643538f41c6a14d6ce8de03771b","30d01de1e8ed4ec7993af216a5e562cf","dbbdd111aaaf4f51af79c9fb8788176f","281d2b81b7004275988c4289fe827054","b01b38ac5d6a41e197ee5b2e679895a2","7949bd9180bd45fd8ca7dcca52117277","4f2a484ba98545d488b4fa9a10966949","91ab2cab1dc949b6b720b5c8bc235fa1","350edc0ef5c546018870c7d25eab3a39","eb6e21452c6140ec9c0dda263b490da5","62ec8cf797e1441cbafe9634d8834315","09f1f5ba5f7349689fdc08f8a89f4e70","79f6a74858964975ae02cdace0ef2f2c","b6fe8edc6bf9413c8fc556d24e52fe3d","8a613043ffa74be9914d63c6977b2e1a","0b186ab2870c4db8907b5898f2ac9158","ba90c8b2af7343c6b479d682d201e144"]},"executionInfo":{"elapsed":660962,"status":"ok","timestamp":1769700980682,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"a6a74f51","outputId":"24231ae7-ea8b-490c-b5e4-3e5be6de783b"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Tokenizing train dataset:   0%|          | 0/800 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"80d6b89f9fda42d9ba93264482b7dc0b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Truncating train dataset:   0%|          | 0/800 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8a6aed98f0ff4a3caf0708ccd45d8fd6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Tokenizing eval dataset:   0%|          | 0/200 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"44fde7cbae52407faff7341a35412503"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Truncating eval dataset:   0%|          | 0/200 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4f2a484ba98545d488b4fa9a10966949"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["The model is already on multiple devices. Skipping the move to device specified in `args`.\n","The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'bos_token_id': 2, 'pad_token_id': 0}.\n","/usr/local/lib/python3.12/dist-packages/notebook/notebookapp.py:191: SyntaxWarning: invalid escape sequence '\\/'\n","  | |_| | '_ \\/ _` / _` |  _/ -_)\n","\u001b[34m\u001b[1mwandb\u001b[0m: (1) Create a W&B account\n","\u001b[34m\u001b[1mwandb\u001b[0m: (2) Use an existing W&B account\n","\u001b[34m\u001b[1mwandb\u001b[0m: (3) Don't visualize my results\n","\u001b[34m\u001b[1mwandb\u001b[0m: Enter your choice:"]},{"name":"stdout","output_type":"stream","text":[" 3\n"]},{"output_type":"stream","name":"stderr","text":["\u001b[34m\u001b[1mwandb\u001b[0m: You chose \"Don't visualize my results\"\n","\u001b[34m\u001b[1mwandb\u001b[0m: Using W&B in offline mode.\n","\u001b[34m\u001b[1mwandb\u001b[0m: W&B API key is configured. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Tracking run with wandb version 0.24.0"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["W&B syncing is set to <code>`offline`<code> in this directory. Run <code>`wandb online`<code> or set <code>WANDB_MODE=online<code> to enable cloud syncing.<br>Run data is saved locally in <code>/content/drive/MyDrive/Learning 📚/YouTube ▶️/Daniel Bourke/gemma3-270m-mermaid/wandb/offline-run-20260129_152822-tztdygf6</code>"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Detected [huggingface_hub.inference, mcp] in use.\n","\u001b[34m\u001b[1mwandb\u001b[0m: Use W&B Weave for improved LLM call tracing. Install Weave with `pip install weave` then add `import weave` to the top of your script.\n","\u001b[34m\u001b[1mwandb\u001b[0m: For more information, check out the docs at: https://weave-docs.wandb.ai/\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='600' max='600' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [600/600 07:53, Epoch 3/3]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Epoch</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>1</td>\n","      <td>0.591300</td>\n","      <td>0.700934</td>\n","    </tr>\n","    <tr>\n","      <td>2</td>\n","      <td>0.406700</td>\n","      <td>0.643714</td>\n","    </tr>\n","    <tr>\n","      <td>3</td>\n","      <td>0.371500</td>\n","      <td>0.678303</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m URL not available in offline run\n","\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m URL not available in offline run\n","\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m URL not available in offline run\n"]},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=600, training_loss=0.569343770518899, metrics={'train_runtime': 654.4007, 'train_samples_per_second': 3.667, 'train_steps_per_second': 0.917, 'total_flos': 271850596426752.0, 'train_loss': 0.569343770518899})"]},"metadata":{},"execution_count":24}],"source":["# Supervised Fine-Tuning = provide input and desired output samples\n","from trl import SFTTrainer\n","\n","# Create Trainer object\n","trainer = SFTTrainer(\n","    model=model,\n","    args=sft_config,\n","    train_dataset=dataset[\"train\"],\n","    eval_dataset=dataset[\"test\"],\n","    processing_class=tokenizer\n",")\n","\n","trainer.train()"]},{"cell_type":"code","execution_count":25,"id":"NaCCMl9Wf75q","metadata":{"executionInfo":{"elapsed":9009,"status":"ok","timestamp":1769700989662,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"NaCCMl9Wf75q"},"outputs":[],"source":["# Save the model\n","trainer.save_model()\n","\n","# Remove all the checkpoint folders (since we've already saved the best model)\n","!rm -rf ./checkpoint_models/checkpoint-*/*\n","!rm -rf ./checkpoint_models/checkpoint-*"]},{"cell_type":"markdown","id":"cfbb9fd0-e0ed-4c18-bfb4-a1ad2e40debd","metadata":{"id":"cfbb9fd0-e0ed-4c18-bfb4-a1ad2e40debd"},"source":["Woohoo! Looks like our training accuracy went up. Let's inspect the **loss curves**."]},{"cell_type":"code","execution_count":26,"id":"0e176da4","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"executionInfo":{"elapsed":435,"status":"ok","timestamp":1769700990098,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"0e176da4","outputId":"c348d3a8-bd36-449f-ea7b-097396ef98a7"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["import matplotlib.pyplot as plt\n","\n","# Access the log history\n","log_history = trainer.state.log_history\n","\n","# Extract training / validation loss\n","train_losses = [log[\"loss\"] for log in log_history if \"loss\" in log]\n","epoch_train = [log[\"epoch\"] for log in log_history if \"loss\" in log]\n","eval_losses = [log[\"eval_loss\"] for log in log_history if \"eval_loss\" in log]\n","epoch_eval = [log[\"epoch\"] for log in log_history if \"eval_loss\" in log]\n","\n","# Plot the training loss\n","plt.plot(epoch_train, train_losses, label=\"Training Loss\")\n","plt.plot(epoch_eval, eval_losses, label=\"Validation Loss\")\n","plt.xlabel(\"Epoch\")\n","plt.ylabel(\"Loss\")\n","plt.title(\"Training and Validation Loss per Epoch\")\n","plt.legend()\n","plt.grid(True)\n","plt.show()"]},{"cell_type":"markdown","id":"1b744778","metadata":{"id":"1b744778"},"source":["## Make Inference\n","\n","We've now fine-tuned our own Gemma 3 270M to do a specific task, let's load it back in and see how it performs."]},{"cell_type":"code","execution_count":27,"id":"c2ebfeae","metadata":{"executionInfo":{"elapsed":1510,"status":"ok","timestamp":1769700991612,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"c2ebfeae"},"outputs":[],"source":["# Load the fine-tuned model and see how it goes\n","from transformers import AutoTokenizer, AutoModelForCausalLM\n","\n","# Load trained model\n","loaded_model = AutoModelForCausalLM.from_pretrained(\n","    pretrained_model_name_or_path=CHECKPOINT_DIR_NAME,\n","    dtype=\"auto\",\n","    device_map=\"auto\",\n","    attn_implementation=\"eager\"\n",");"]},{"cell_type":"code","execution_count":28,"id":"739c608d","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":14,"status":"ok","timestamp":1769700991629,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"739c608d","outputId":"227de4a6-7b3a-43c5-fbd1-b6cc4d9b3396"},"outputs":[{"output_type":"stream","name":"stderr","text":["Device set to use cuda:0\n"]},{"output_type":"execute_result","data":{"text/plain":["<transformers.pipelines.text_generation.TextGenerationPipeline at 0x7adaf8d50110>"]},"metadata":{},"execution_count":28}],"source":["loaded_model_pipeline = pipeline(\"text-generation\",\n","                                 model=loaded_model,\n","                                 tokenizer=tokenizer)\n","\n","loaded_model_pipeline"]},{"cell_type":"code","execution_count":29,"id":"8db929ad","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1769700991636,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"8db929ad","outputId":"ba39bc88-2ae6-4819-9b71-01c2ba92100e"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Dataset({\n","    features: ['input', 'output', 'messages'],\n","    num_rows: 200\n","})"]},"metadata":{},"execution_count":29}],"source":["dataset[\"test\"]"]},{"cell_type":"markdown","id":"5fa55181-5aba-47f4-b237-da7fcb57bfd5","metadata":{"id":"5fa55181-5aba-47f4-b237-da7fcb57bfd5"},"source":["Let's now perform inference with our fine-tuned model on a random sample from the test dataset (our model has never seen these samples)."]},{"cell_type":"code","execution_count":30,"id":"ee88d7fa","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4180,"status":"ok","timestamp":1769700995818,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"ee88d7fa","outputId":"b7d47500-8b75-4392-a77b-728070459c77"},"outputs":[{"output_type":"stream","name":"stdout","text":["[INFO] Input:\n","<bos><start_of_turn>user\n","Design a diagram representing the workflow of a disaster recovery plan implementation. Ensure it includes steps such as risk assessment, plan development, testing, recovery operations, and post-recovery assessment.<end_of_turn>\n","<start_of_turn>model\n","\n","\n","[INFO] Output:\n","graph TD;\n","    A[Risk Assessment] --> B[Plan Development];\n","    B --> C[Testing];\n","    C --> D[Recovery Operations];\n","    D --> E[Post-Recovery Assessment];\n"]}],"source":["# Get a random sample\n","random_test_idx = get_random_idx(dataset[\"test\"])\n","random_test_sample = dataset[\"test\"][random_test_idx]\n","\n","# Apply the chat template\n","input_prompt = pipe.tokenizer.apply_chat_template(conversation=random_test_sample[\"messages\"][:1],\n","                                                  tokenize=False,\n","                                                  add_generation_prompt=True)\n","\n","# Let's run the fine-tuned model on our input\n","default_outputs = loaded_model_pipeline(text_inputs=input_prompt,\n","                                        max_new_tokens=256)\n","\n","# View and compare the outputs\n","print(f\"[INFO] Input:\\n{input_prompt}\\n\")\n","print(f\"[INFO] Output:\\n{default_outputs[0]['generated_text'][len(input_prompt):]}\")"]},{"cell_type":"code","execution_count":31,"id":"1f424585","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1769700995827,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"1f424585","outputId":"422445b1-338e-4803-f7e7-726ba75a812b"},"outputs":[{"output_type":"stream","name":"stdout","text":["graph TD;\n","    A[Risk Assessment] --> B[Plan Development];\n","    B --> C[Testing];\n","    C --> D[Recovery Operations];\n","    D --> E[Post-Recovery Assessment];\n"]}],"source":["print(random_test_sample[\"output\"])"]},{"cell_type":"markdown","id":"2efdc312-31c9-45cb-93f3-c9844d832b31","metadata":{"id":"2efdc312-31c9-45cb-93f3-c9844d832b31"},"source":["## Model Size"]},{"cell_type":"code","execution_count":32,"id":"56c4e8ce","metadata":{"executionInfo":{"elapsed":8,"status":"ok","timestamp":1769700995836,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"56c4e8ce","colab":{"base_uri":"https://localhost:8080/"},"outputId":"6b0f11d3-8a9a-4e6d-8bf8-887becb9d1b4"},"outputs":[{"output_type":"stream","name":"stdout","text":["Trainable parameters: 268,098,176\n","Non-trainable parameters: 0\n","Total parameters: 268,098,176\n"]}],"source":["def get_model_num_params(model):\n","    \"\"\"\n","    Returns the number of trainable, non-trainable and total parameters of a PyTorch model.\n","    \"\"\"\n","    trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n","    non_trainable_params = sum(p.numel() for p in model.parameters() if not p.requires_grad)\n","    total_params = trainable_params + non_trainable_params\n","    return {\"trainable_params\": trainable_params,\n","            \"non_trainable_params\": non_trainable_params,\n","            \"total_params\": total_params}\n","\n","# Get parameters of our fine-tuned model\n","model_params = get_model_num_params(loaded_model)\n","print(f\"Trainable parameters: {model_params['trainable_params']:,}\")\n","print(f\"Non-trainable parameters: {model_params['non_trainable_params']:,}\")\n","print(f\"Total parameters: {model_params['total_params']:,}\")"]},{"cell_type":"code","execution_count":33,"id":"10c365fe","metadata":{"executionInfo":{"elapsed":4,"status":"ok","timestamp":1769700995841,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"10c365fe","colab":{"base_uri":"https://localhost:8080/"},"outputId":"ef0419ab-4c3a-497a-abf7-4e7e8261ceec"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["444.44444444444446"]},"metadata":{},"execution_count":33}],"source":["# Our model is 270M parameters, GPT-OSS-120B is 120B parameters\n","120_000_000_000 / 270_000_000"]},{"cell_type":"markdown","id":"7091522a-816b-48c2-b15b-2b163471fa00","metadata":{"id":"7091522a-816b-48c2-b15b-2b163471fa00"},"source":["By fine-tuning Gemma 3 270M we distill the capabilities of a 120B parameter model into a model 444x smaller."]},{"cell_type":"markdown","id":"79c61ef3","metadata":{"id":"79c61ef3"},"source":["## Uploading to the HF Hub\n","\n","We can upload our fine-tuned model to the Hugging Face Hub so other people can use it and test it out.\n","\n","First, let's load it in ourselves and confirm it works how we'd like it to."]},{"cell_type":"code","execution_count":34,"id":"60366c1e","metadata":{"executionInfo":{"elapsed":3650,"status":"ok","timestamp":1769700999492,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"60366c1e","colab":{"base_uri":"https://localhost:8080/"},"outputId":"6694f1db-f96a-4261-cf42-d326281efc02"},"outputs":[{"output_type":"stream","name":"stderr","text":["The module name  (originally ) is not a valid Python identifier. Please rename the original module to avoid import issues.\n","The tokenizer you are loading from './checkpoint_models/' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue.\n","Device set to use cuda:0\n"]},{"output_type":"execute_result","data":{"text/plain":["[{'generated_text': '\\nDesign a composite structure diagram for a car navigation system.\\nInclude components such as GPS Module, Display Unit, Control Module, and illustrate how these components interact internally.\\n'}]"]},"metadata":{},"execution_count":34}],"source":["from transformers import AutoModelForCausalLM, AutoTokenizer\n","from transformers import pipeline\n","\n","MODEL_PATH = \"./checkpoint_models/\"\n","\n","# Load the model into a pipeline\n","loaded_model = AutoModelForCausalLM.from_pretrained(\n","    pretrained_model_name_or_path=MODEL_PATH,\n","    dtype=\"auto\",\n","    device_map=\"auto\",\n","    attn_implementation=\"eager\"\n",")\n","\n","tokenizer = AutoTokenizer.from_pretrained(\n","    MODEL_PATH\n",")\n","\n","# Create a pipeline from the loaded model\n","loaded_model_pipeline = pipeline(\"text-generation\",\n","                                 model=loaded_model,\n","                                 tokenizer=tokenizer)\n","\n","# Test the loaded model on raw text (this won't work as well as formatted text)\n","test_input_message = \"\"\"\n","Design a composite structure diagram for a car navigation system.\n","Include components such as GPS Module, Display Unit, Control Module, and illustrate how these components interact internally.\n","\"\"\"\n","loaded_model_pipeline(test_input_message)"]},{"cell_type":"markdown","id":"ae54a14a-cdf2-49f5-8a43-7fd555a5642c","metadata":{"id":"ae54a14a-cdf2-49f5-8a43-7fd555a5642c"},"source":["Let's create a helper function to format our input text into message format."]},{"cell_type":"code","execution_count":35,"id":"4546c352-9764-4cff-b1e9-3db235a3b1b1","metadata":{"executionInfo":{"elapsed":9,"status":"ok","timestamp":1769700999503,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"4546c352-9764-4cff-b1e9-3db235a3b1b1","colab":{"base_uri":"https://localhost:8080/"},"outputId":"94f21d6f-ad97-4b0c-bba6-c6897351b9f2"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[{'role': 'user',\n","  'content': '\\nDesign a composite structure diagram for a car navigation system.\\nInclude components such as GPS Module, Display Unit, Control Module, and illustrate how these components interact internally.\\n'}]"]},"metadata":{},"execution_count":35}],"source":["def format_message(input):\n","    return [{\"role\": \"user\", \"content\": input}]\n","\n","input_formatted = format_message(input=test_input_message)\n","input_formatted"]},{"cell_type":"markdown","id":"cf6251b6-09b1-44e2-a419-58e2b315a83d","metadata":{"id":"cf6251b6-09b1-44e2-a419-58e2b315a83d"},"source":["Now we can turn it into a prompt with our `tokenizer` and the `apply_chat_template` method."]},{"cell_type":"code","execution_count":36,"id":"c7686504-0d33-4751-a8b4-55107719ef9d","metadata":{"executionInfo":{"elapsed":3,"status":"ok","timestamp":1769700999507,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"c7686504-0d33-4751-a8b4-55107719ef9d","colab":{"base_uri":"https://localhost:8080/","height":70},"outputId":"59c1c166-f6ef-4233-808d-f04c1b50fb94"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'<bos><start_of_turn>user\\nDesign a composite structure diagram for a car navigation system.\\nInclude components such as GPS Module, Display Unit, Control Module, and illustrate how these components interact internally.<end_of_turn>\\n<start_of_turn>model\\n'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":36}],"source":["input_prompt = loaded_model_pipeline.tokenizer.apply_chat_template(conversation=input_formatted,\n","                                                                   tokenize=False,\n","                                                                   add_generation_prompt=True)\n","\n","input_prompt"]},{"cell_type":"markdown","id":"586b36c3-9677-408c-a8a9-5925e5043d71","metadata":{"id":"586b36c3-9677-408c-a8a9-5925e5043d71"},"source":["Beautiful! Time to run our fine-tuned model!"]},{"cell_type":"code","execution_count":37,"id":"41583a8d-13a8-4d57-b7d4-99f7c6619843","metadata":{"executionInfo":{"elapsed":4728,"status":"ok","timestamp":1769701004236,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"41583a8d-13a8-4d57-b7d4-99f7c6619843","colab":{"base_uri":"https://localhost:8080/"},"outputId":"e2048735-7511-402a-d4eb-67b88d49e6c7"},"outputs":[{"output_type":"stream","name":"stdout","text":["[INFO] Input:\n","<bos><start_of_turn>user\n","Design a composite structure diagram for a car navigation system.\n","Include components such as GPS Module, Display Unit, Control Module, and illustrate how these components interact internally.<end_of_turn>\n","<start_of_turn>model\n","\n","\n","[INFO] Output:\n","composite\n","    A[GPS Module] --> B[Display Unit]\n","    B --> C[Control Module]\n","    B --> D[Control Panel]\n","    B --> E[Navigation System]\n","    B --> F[Database]\n","    B --> G[API Gateway]\n","    B --> H[Controller]\n"]}],"source":["loaded_model_outputs = loaded_model_pipeline(text_inputs=input_prompt,\n","                                             max_new_tokens=256)\n","\n","# View and compare the outputs\n","print(f\"[INFO] Input:\\n{input_prompt}\\n\")\n","print(f\"[INFO] Output:\\n{loaded_model_outputs[0]['generated_text'][len(input_prompt):]}\")"]},{"cell_type":"markdown","id":"83a05457-44f7-4a1b-8e4c-2ee6b49ff42a","metadata":{"id":"83a05457-44f7-4a1b-8e4c-2ee6b49ff42a"},"source":["Okay let's make another helper function to predict on any given sample input.\n","\n","We'll also return the inference time of our model so we can see how long things take."]},{"cell_type":"code","execution_count":38,"id":"2341bd46-23d7-424a-b59d-d1d52481890b","metadata":{"executionInfo":{"elapsed":4369,"status":"ok","timestamp":1769701008610,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"2341bd46-23d7-424a-b59d-d1d52481890b","colab":{"base_uri":"https://localhost:8080/"},"outputId":"60e1ad4d-6ab7-4022-95c6-0af3eafcb3a3"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["('composite\\n  A[GPS Module] --> B[Display Unit]\\n  B --> C[Control Module]\\n  C --> D[Control Panel]\\n  D --> E[Sensor Network]\\n  E --> A',\n"," [{'generated_text': [{'role': 'user',\n","     'content': '\\nDesign a composite structure diagram for a car navigation system.\\nInclude components such as GPS Module, Display Unit, Control Module, and illustrate how these components interact internally.\\n'},\n","    {'role': 'assistant',\n","     'content': 'composite\\n  A[GPS Module] --> B[Display Unit]\\n  B --> C[Control Module]\\n  C --> D[Control Panel]\\n  D --> E[Sensor Network]\\n  E --> A'}]}],\n"," 4.3156)"]},"metadata":{},"execution_count":38}],"source":["import time\n","\n","def pred_on_text(input_text):\n","    start_time = time.time()\n","\n","    raw_output = loaded_model_pipeline(text_inputs=[{\"role\": \"user\",\n","                                                    \"content\": input_text}],\n","                                       max_new_tokens=256,\n","                                       disable_compile=True)\n","    end_time = time.time()\n","    total_time = round(end_time - start_time, 4)\n","\n","    generated_text = raw_output[0][\"generated_text\"][1][\"content\"]\n","\n","    return generated_text, raw_output, total_time\n","\n","pred_on_text(input_text=test_input_message)"]},{"cell_type":"markdown","id":"ae3716aa-7eea-433d-b002-d3f9b1270784","metadata":{"id":"ae3716aa-7eea-433d-b002-d3f9b1270784"},"source":["Nice! Looks like our model is working well enough (of course we could always improve it over time with more testing and different samples).\n","\n","Let's upload it to the Hugging Face Hub. We can do so using the [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/en/index) library."]},{"cell_type":"code","execution_count":39,"id":"b536ac2f-fd95-441f-a708-b1b685e07e33","metadata":{"executionInfo":{"elapsed":34713,"status":"ok","timestamp":1769701043326,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"b536ac2f-fd95-441f-a708-b1b685e07e33","colab":{"base_uri":"https://localhost:8080/","height":345,"referenced_widgets":["a5638f01964849efbaca5d35082a1194","966e6cff2e4144e2833fcbe87de497e6","efbbfc07e21b40608dfefde31e2dfd56","44df920a0fea418c9e6212504cc127df","04a641aba72a4544a323f6ce18dc0aa5","11ed671fbd91481490ccc0da65b3a280","13cb66a96f5146f79615166f10d7dbe9","e93c3943920449d3b14d387859434f99","b7edfd1cfa3e4e5a804c94326b0f535b","eca0cb9fa3174999a851c7555c601f8f","f9cff0d0fd334c03a2af9e5111ab479b","af14b9fdd6464e139f5ff5f7072a5760","594b9247dfd64e47b378a97ff1e3e565","be1255fb83694b98adc5e9f6c820c0f3","2969ff6c783c4aa9a14006a2fa0eefd1","651b58077b354475aa37d8dc4b1cf2a5","cddd28e8c16c44af8bf1b6d72ca22bc4","31a8f3c466184d379a64076e81f9015d","eb8f24df285944a0b99d975437cb7156","ecb9f24b59604717b3aaa2713c3f0543","59550ed24a72432cb86c27c2cd94bfd5","6ae3a7e7540d4b949e3d01976c3f127a","cf191b5e8c4f46bea154111a8afdc2c1","387d484cf2c746adbf4d974b657e9797","2d2d9ed2f153489b8ff33f32d23bf91c","918479e56d134a339b09b72579db2b96","ae5c3198a88d4ec2b03e1b3b3fc945d3","c7e7b664bc11434fbeaecb8f7f90d134","cb3cccdb4dec46818c97ceb1023a9749","246e3c8dd07e4de099d0886821b4f9b7","75a627c45f6b46e8984f10332bb6729b","bd78a5015b8f42a099655aa9b8af9c3e","fb93141f91834f1a9ab4f3a89d49a780","46a225f36b7a409a819e8874797ed666","12be9a8b164d4e8287c8bef1d0f5164c","b3bf5ce3e26846c89bc5109a6380af72","096779338825421a92f72f58a18f181e","54c076fe4d74482d8c72ce28126ad2dc","303d4512678149a399fefda9581d89e1","f9a41cc6922848bfbf10c6fa8c7cfde2","5fb514e4b9394a3eae35c7c41c468621","1b8bf50957ef45d499865121a8f96646","67312077102c4658bf14437622858f88","1deca089964846bf9b2304962351d5ac","46d553ade73b4f358209a492bcb1355e","9d658c372e404fa5857af11f3b8e6c00","dc75296126a347b3a54303fb1f61c31a","6a86a93792d5493a9656e2e601bbebcf","a05b57f11cc44fd09c7c0f75fb9ba35e","bc1c56ca716f48819490a361bffd559b","6e0b4747e2a547f6a7ff4b887865949a","d51624a144a84cc88ca0fb714074b972","27a664829b4f457789175ae4243bfb9d","c6299db30d19445d8a37cc138f23f384","1df53275175e46e28da95245febcd902","9deb44627507431195366636562ca12a","2b4b8e56655c473fb36d3fcff4874273","341f279a9166474a98772e7c51a40348","5cceda6a29914b4785870a45fe352548","3ad80c23b9b4492eac529dd3ca0f70bf","73409d0e0ae243c197bb6bc58c11fc22","5185b917d1e343918fadeeee4481732f","c46ebe19f36049bd925ad345c9bc9b8b","4067f499cce94ec2aa1df97c117a27bd","217bffbfc78b4882bac934420453b358","6c15653391cb4c99aa0035bac7010e5c","5651e654bbc94a6d8e4cc7d6034d261e","ebecf58709644ba3a2a2808024ca994d","61ca9db9e9c948c9991d4c494327b6d0","9e9ed30afaaf48f8940b5e6a596ac8c4","48dc33e9556949b9a5902b8411ed3988","4021a92e189d46c3a254f9f23f29cbe8","65f3c1855fa147199c49816768cf80fc","8bb5a268aafa4d0ab3c0a286d24e2a0f","b45d7a6b5b1949a59cd2f8f245c5613b","237ea41551834351aa4c96fff69828a8","aeebb38ead5f460294ca3023cef926d7"]},"outputId":"57e855b3-de4a-40cf-ba0c-37060a8756fe"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Processing Files (0 / 0)      : |          |  0.00B /  0.00B            "],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a5638f01964849efbaca5d35082a1194"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["New Data Upload               : |          |  0.00B /  0.00B            "],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"af14b9fdd6464e139f5ff5f7072a5760"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  ..._models/model.safetensors:   0%|          |  604kB /  536MB            "],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cf191b5e8c4f46bea154111a8afdc2c1"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  ...nt_models/tokenizer.model: 100%|##########| 4.69MB / 4.69MB            "],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"46a225f36b7a409a819e8874797ed666"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  ...int_models/tokenizer.json:  50%|#####     | 16.7MB / 33.4MB            "],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"46d553ade73b4f358209a492bcb1355e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  ...700325.609a3bb50941.883.0:   6%|6         | 14.9kB /  236kB            "],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9deb44627507431195366636562ca12a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  ..._models/training_args.bin:   6%|6         |   395B / 6.29kB            "],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5651e654bbc94a6d8e4cc7d6034d261e"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["CommitInfo(commit_url='https://huggingface.co/MrObiKenobi/Mermaid-gemma-3-270m-it/commit/1b3531b1b2a8b04aa4fe963e5903b52baebfcd42', commit_message='Upload folder using huggingface_hub', commit_description='', oid='1b3531b1b2a8b04aa4fe963e5903b52baebfcd42', pr_url=None, repo_url=RepoUrl('https://huggingface.co/MrObiKenobi/Mermaid-gemma-3-270m-it', endpoint='https://huggingface.co', repo_type='model', repo_id='MrObiKenobi/Mermaid-gemma-3-270m-it'), pr_revision=None, pr_num=None)"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":39}],"source":["from huggingface_hub import HfApi, create_repo\n","\n","api = HfApi()\n","\n","# Give our model a name (this is in the format [Hugging Face Username]/[Target Model Name]\n","repo_id = \"MrObiKenobi/mermaid-gemma-3-270m-it\"\n","\n","# Create the repo\n","create_repo(repo_id,\n","            repo_type=\"model\",\n","            exist_ok=True)\n","\n","# Upload the entire model folder containing our model files\n","api.upload_folder(\n","    folder_path=\"./checkpoint_models/\",\n","    repo_id=repo_id,\n","    repo_type=\"model\"\n",")"]},{"cell_type":"markdown","id":"a2b2f73f-ba69-472c-b08b-9ec5ee360508","metadata":{"id":"a2b2f73f-ba69-472c-b08b-9ec5ee360508"},"source":["Woohoo! Our model is officially on the Hugging Face Hub.\n","\n","Now not only can we redownload it and use it again, others can download it and use it for themselves (of course you can make the model private if you like too)."]},{"cell_type":"markdown","id":"afeae0a6-4384-41ea-a2e7-88ec31fdeddf","metadata":{"id":"afeae0a6-4384-41ea-a2e7-88ec31fdeddf"},"source":["## [Bonus] Gradio demo\n","\n","Right now our model seems to be working quite well for our specific use case. However, it takes some coding to be able to use it. What if we wanted to allow someone who wasn't familiar with programming to try it out?\n","\n","To do so, we can turn our model into a [**Gradio demo**](https://www.gradio.app) and upload it to [**Hugging Face Spaces**](https://huggingface.co/spaces) (a place to share all kinds of small applications). Gradio allows us to turn our model into an easy to use and sharable demo anyone can try.\n","\n","\n","To create a sharable demo, we'll need the following files:\n","\n","- `app.py` - Entry point for our app, all of our application code will go in here.\n","- `README.md` - Tells people what our app does.\n","- `requirements.txt` - Tells Hugging Face Spaces what our app requires."]},{"cell_type":"markdown","id":"bc6216a5-db4d-4220-a18a-f378b4490a9d","metadata":{"id":"bc6216a5-db4d-4220-a18a-f378b4490a9d"},"source":["## [Bonus] Batched inference\n","\n","Right now our model only inferences on one sample at a time but as is the case with many machine learning models, we could **perform inference on multiple samples** (also referred to as a batch) to significantly **improve throughput**.\n","\n","> *Throughput* → how many samples/sec you process\n",">\n","> *Latency* → how long one request takes\n",">\n","> *Batching* → improves throughput, may increase latency\n","\n","The number of samples you can predict on at once will depend on a few **factors**:\n","-  The size of your model (e.g. if your model is quite large, it may only be able to predict on 1 sample at time)\n","- The size of your compute VRAM (e.g. if your compute VRAM is already saturated, add multiple samples at a time may result in errors)\n","- The size of your samples (if one of your samples is 100x the size of others, this may cause errors with batched inference)\n","\n","To **find an optimal batch size** for our setup, we can loop through different batch sizes and measure the throughput for each batch size.\n","\n","Why do we do this?\n","- It's hard to tell the ideal batch size ahead of time.\n","- So we experiment from say 1, 2, 4, 8, 16, 32, 64 batch sizes and see which performs best.\n","- Just because we may get a speed up from using batch size 8, doesn't mean 64 will be better."]},{"cell_type":"code","execution_count":40,"id":"74535c69-d30a-4d95-aa0d-b477ac0d6f2c","metadata":{"executionInfo":{"elapsed":6149,"status":"ok","timestamp":1769701049479,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"74535c69-d30a-4d95-aa0d-b477ac0d6f2c","colab":{"base_uri":"https://localhost:8080/","height":0,"referenced_widgets":["65f16eb019714dcd8ccb5f1a2f4a8f34","51dd5fe6238f4ab99294117873978653","ec82fc00b29544da9ed795ddc8a462c9","a72d1f56448e40cabec749052916840f","ecec709addfe4cb3b359d73d4c4f3e1e","2d6ec12c3ee149e4aa0ac6b9ff64fbb5","217d99a851dd46d49b1004694762bc41","47f22b531bcc4d35877900c246e04a70","b4e31787fbef412ead9151e065a1e4c8","3b1049fd23244ac09cebcb3886761fdf","f15f7ccc2ade44a282b1e62c5c285de7","33e0354187214b75832db1ce90541261","7a50be56a2ab4390a1328d5b6680143f","687ea6387e3b4a09a42595a678ad7504","c61d34a29d5b4a259ce7fba05b967909","fe77d1575f0f4174b206ca51bd7b2bf4","648f54ab55b540608b8ce2eb9935479b","14c6abbbe66c4ca18ee09b14ef6efaf2","57e76336bd324082ba04b1fc40d0e807","017f591c230f41329cc18f5fbd7ad29d","73a2ad5e43e541768fd0fd158450c55e","b6e18f3fb6cf4078a909a6cfa98c5289","7aa7969255114720b7c760690cfb2f05","3b8ea6274d9a47ba9515fd9107043ffd","797f7bba6dba4e378bac74e6ee4cada6","0929f4718a874eea917831d4dc5c004d","404ff4cf371e44edb2048d1eeb4436e0","74487d05f1e54287abbb3ff1b1ecbc80","6ef03d3b35a5494a865d9ba037979069","c62490f4810646328792189ff99b4189","0f3d0ae690cb401eae78a6b9fa9dd6ab","87bd09467b2946e08135fe93eefcdba2","1f803234a69347a4a4338123869f5ccc","1c6de9fb18164e269995b91c95eefe97","5b300f8a75f94e27a0b8118be77ce8ae","4a720e82c99640009442609f9b2f9198","2bcbcfc8552d4739aefccbd5714fa3fd","0f2f669c169c4a9f806008404f103198","5cbe941cb0bb4dbaa4d07b7f28502b56","c34dca0e20174021876bd9ab456ddde4","d0f6d816002f4c32b6f1ca2bc42b8459","97d6bf52862546ec992595a3404cd1f5","224f9c474f8848f5a5129de550c99d29","f498d946908f49d690e11cf5d4d3a9e0"]},"outputId":"c2820c6a-5d64-46cb-e13a-505980d5fb0d"},"outputs":[{"output_type":"display_data","data":{"text/plain":["README.md: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"65f16eb019714dcd8ccb5f1a2f4a8f34"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["data.csv: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"33e0354187214b75832db1ce90541261"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split:   0%|          | 0/132 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7aa7969255114720b7c760690cfb2f05"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["[INFO] Number of samples in the dataset: 132\n"]},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/132 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1c6de9fb18164e269995b91c95eefe97"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['nl_task_title', 'nl_task_desc', 'input', 'output', 'messages'],\n","        num_rows: 132\n","    })\n","})"]},"metadata":{},"execution_count":40}],"source":["from datasets import load_dataset\n","\n","dataset = load_dataset(\"ibm-research/MermaidSeqBench\")\n","\n","print(f\"[INFO] Number of samples in the dataset: {len(dataset['train'])}\")\n","\n","# Normalize dataset column names\n","dataset = dataset.rename_columns({\n","    \"input_prompt\": \"input\",\n","    \"expected_output\": \"output\",\n","})\n","\n","def sample_to_conversation(sample):\n","    return {\n","        \"messages\": [\n","            {\"role\": \"user\", \"content\": sample[\"input\"]},\n","            {\"role\": \"system\", \"content\": sample[\"output\"]}\n","        ]\n","    }\n","\n","# Map our sample_to_conversation function to dataset\n","dataset = dataset.map(sample_to_conversation,\n","                      batched=False)\n","\n","dataset"]},{"cell_type":"code","execution_count":41,"id":"33695408-65b3-4708-a008-30acd385f46c","metadata":{"executionInfo":{"elapsed":15,"status":"ok","timestamp":1769701049495,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"33695408-65b3-4708-a008-30acd385f46c","colab":{"base_uri":"https://localhost:8080/","height":0},"outputId":"b898be2e-eabc-46f3-f1d6-fe3da3cf9de2"},"outputs":[{"output_type":"stream","name":"stdout","text":["[INFO] Formatting test samples into list prompts...\n","[INFO] Number of test sample prompts: 132\n"]},{"output_type":"execute_result","data":{"text/plain":["'<bos><start_of_turn>user\\nGenerate a valid, syntactically correct MermaidJS UML\\nsequence diagram based on the provided structured input:\\n\\nInput Format:\\n- Purpose: Description of the diagram\\n- Main Components: List of involved components\\n- Interactions: Sequence of interactions\\n\\nGeneral Guidelines:\\n- Identify interactions between the Main Components, distinguishing requests (->>) and responses (-->>). \\n- Structure participants in this order: Users -> Frontend (Mobile/Web) -> Backend (BFF, Middleware, SOR, DB).\\n- Determine all the alternate cases needed, if-else, alt, and make sure they are all covered.\\n- Make sure there is a response to every request.\\n- Format API calls as REST API (e.g., `GET /Workorder(params)`, `POST /Workorder(params)`).\\n- Handle alternate flows (if, if-else, nested conditions) per MermaidJS syntax.\\n- Ensure syntax correctness, covering all responses, error scenarios, and system failures.\\n- Use activation (`activate` actor) and deactivation (`deactivate` actor) properly.\\n- Add notes where applicable.\\n\\nMermaidJS Syntax Rules:\\n- Begin with \\'sequenceDiagram\\'.\\n- Use \\'participant ActorName\\' to introduce actors.\\n- Activate (\\'activate\\') actors when they receive messages; deactivate (\\'deactivate\\') after responding.\\n- Treat alternate case like \"if{}\", \"if{} else {}\", nested if statements has to be handled.\\n- Ensure that any actor activated before an alternate flow must deactivate after it ends.\\n- Follow structured handling of conditional cases (see Example).\\n- Add notes over the actor only when required. Notes should address the question of \"why\". Notes can also be used to segregate groups of request/response flows. Example: `Note over Actor: notes content`\\n- Respond only with the MermaidJS code, formatted as markdown. Do not include explanations or additional text.\\n\\nExample Input:\\nPurpose: Client-Server Request Handling\\nMain Components: Client (CT), Server (SR)\\nInteractions:\\nClient Action: The client sends a request with data (X) to the server.\\nServer Processing:\\n    - The server receives the request and processes it.\\n    - If the request is valid, the server sends a successful response with data (Y).\\n    - If the request is invalid, the server returns an error message indicating invalid data.\\nResponse:\\n    - Upon successful processing, the client receives the requested data.\\n    - If an error occurs, the client is notified accordingly.\\n\\nExample Output:\\n```sequenceDiagram\\nparticipant CT as Client\\nparticipant SR as Server\\nNote over CT: Sending a request\\nCT->>SR: Request (Data: X)\\nactivate SR\\nalt Successful Response\\n    SR-->>CT: Response (Data: Y)\\nelse Error Case\\n    SR-->>CT: Error (Invalid Data)\\nend\\ndeactivate SR```\\n\\nPurpose: Uploading Documents with Secure Storage\\nMain Components: User, Mobile App, BFF, Azure AD, Database\\n\\nInteractions:\\n\\nUser Action: The user uploads a document (e.g., an ID or contract) through the mobile app.\\nMobile App: Sends the document along with the session token to the BFF.\\nBFF Validation:\\nValidates the session token with Azure AD.\\nChecks user permissions to ensure they are authorized to upload documents.\\nStorage Process:\\nThe BFF saves metadata about the document (e.g., file name, size, upload timestamp) in the database.\\nThe actual document is securely stored in cloud storage (e.g., Azure Blob Storage).\\nResponse:\\nOn successful upload, the BFF returns a confirmation to the app.\\nIf the user is unauthorized or the file exceeds size limits, an appropriate error is returned.<end_of_turn>\\n<start_of_turn>model\\n'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":41}],"source":["# Step 1: Need to turn our samples into batches (e.g. lists of samples)\n","print(f\"[INFO] Formatting test samples into list prompts...\")\n","test_input_prompts = [\n","    loaded_model_pipeline.tokenizer.apply_chat_template(\n","        item[\"messages\"][:1],\n","        tokenize=False,\n","        add_generation_prompt=True\n","    )\n","    for item in dataset[\"train\"]\n","]\n","print(f\"[INFO] Number of test sample prompts: {len(test_input_prompts)}\")\n","test_input_prompts[0]"]},{"cell_type":"code","execution_count":42,"id":"63b3e66c-a5ba-4056-bd9a-97cc9aab7f74","metadata":{"executionInfo":{"elapsed":927852,"status":"ok","timestamp":1769701977351,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"63b3e66c-a5ba-4056-bd9a-97cc9aab7f74","colab":{"base_uri":"https://localhost:8080/","height":0,"referenced_widgets":["e3c7227fa7a449eeb90439ff176cf06c","e166ec76d3144064950eaa4a76280fa1","21a5d0579d564a37b0e0231e08a2512f","33b696beb40043ce949271ee51c27b10","6d053a43b61645c6b05c76d918b5140d","ecec0728e55742728f4451277f91eb86","d8e819baa8b04c9ebac80df1d3cab9e6","1de5683da21f45c29003ddd8b1dcee7a","e2a5ca822ee3416eabae0ee0c762e0b0","158a71c46d52400bbf859190a8f37043","aaf305cd7af04cb3b06ae02bcc2cc57f","5dfaf6ebb14d4822928fbc37903e0892","21dc90b1633c4395bf9664a01e2a2d99","e6b56409a0aa45d0b0ebb1891539a18d","43f1aeb10041497db47f1b44609f63ed","979280dec4b74de6875d7c409ec52490","a7bb671268bb4f3da2f1bd52f41e9628","d5a3c2c86de944ddae94989d3f05e23c","f4a3898753e94749a39c6e08c34a4242","a2a38832b4914b66b72090790fbfc16a","135689c7dccb4f50ad717435e64a9cb4","6eee771e9fe34192a9f56b8236afe06d","902f5950913d47498492353350ffe3ba","9fd781c885d840bb9f4edd22fdfcc900","456883521afa4439a74b29ff88a3b0af","e98c3ffce1114e82b561ac3955f97df4","1414c3d09f4e4b25b13a651211b46c57","2e88b11a30e14d258dae2e5259f52674","4aa18e15734146538a95381706e24cad","1bc096611e6747e0987fb8ec810193d0","8060cd78a5924e1f861f7d3b83e88315","0c84b009231e469fb773d1c558184a10","4ff9d863cd2c44ef921bc74afb99ff0e","bba17f6b427c4f3fa64b4e1796edefa9","0455304c50584711a7c9e3882f3179b9","06679ba5aa95473cb0b2398fc5c1febd","ff638d50902548a48113720130109482","a5a1b8a09dfd4fbfabf0fcd31aa9fb49","747cf6d65b7e49179558c2df952b4efd","11eabcf8c80145a59e189755caf6e48b","6d626b0c3bd44eff924991b0e4980817","4ff86624ce5b44b3be38e8aa3035605f","6494dc5d523248019217340ed3e3dfee","42f4d46ea764462880e1396ca5d5d2e9"]},"outputId":"8fef3e16-3500-4984-ab42-0526ef911a33"},"outputs":[{"output_type":"stream","name":"stdout","text":["[INFO] Making predictions with batch size: 1\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/132 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e3c7227fa7a449eeb90439ff176cf06c"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n"]},{"output_type":"stream","name":"stdout","text":["\n","[INFO] Total time for batch size 1: 534.67s\n","================================================================================\n","\n","\n","[INFO] Making predictions with batch size: 4\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/33 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5dfaf6ebb14d4822928fbc37903e0892"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","[INFO] Total time for batch size 4: 208.87s\n","================================================================================\n","\n","\n","[INFO] Making predictions with batch size: 8\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/16 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"902f5950913d47498492353350ffe3ba"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","[INFO] Total time for batch size 8: 110.92s\n","================================================================================\n","\n","\n","[INFO] Making predictions with batch size: 16\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/8 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"bba17f6b427c4f3fa64b4e1796edefa9"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","[INFO] Total time for batch size 16: 73.36s\n","================================================================================\n","\n","\n"]}],"source":["# Step 2: Need to perform batched inference and time each step\n","import time\n","from tqdm.auto import tqdm\n","\n","all_outputs = []\n","\n","# Let's write a list of batch sizes to test\n","chunk_sizes_to_test = [1, 4, 8, 16] # ... 32, 64, 128\n","timing_dict = {}\n","\n","# Loop through each batch size and time the inference\n","for CHUNK_SIZE in chunk_sizes_to_test:\n","    print(f\"[INFO] Making predictions with batch size: {CHUNK_SIZE}\")\n","    start_time = time.time()\n","\n","    for chunk_number in tqdm(range(round(len(test_input_prompts) / CHUNK_SIZE))):\n","        batched_inputs = test_input_prompts[(CHUNK_SIZE * chunk_number): CHUNK_SIZE * (chunk_number + 1)]\n","        batched_outputs = loaded_model_pipeline(text_inputs=batched_inputs,\n","                                                batch_size=CHUNK_SIZE,\n","                                                max_new_tokens=256,\n","                                                disable_compile=True)\n","\n","        all_outputs += batched_outputs\n","\n","    end_time = time.time()\n","    total_time = end_time - start_time\n","    timing_dict[CHUNK_SIZE] = total_time\n","    print()\n","    print(f\"[INFO] Total time for batch size {CHUNK_SIZE}: {total_time:.2f}s\")\n","    print(\"=\"*80 + \"\\n\\n\")"]},{"cell_type":"markdown","id":"6cf064ff-a84e-4d01-b377-60c0a62813e5","metadata":{"id":"6cf064ff-a84e-4d01-b377-60c0a62813e5"},"source":["Batched inference complete! Let's make a plot comparing different batch sizes."]},{"cell_type":"code","execution_count":43,"id":"b29098f5-13ff-47ee-b1bd-13ec50aa914f","metadata":{"executionInfo":{"elapsed":539,"status":"ok","timestamp":1769701977891,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"b29098f5-13ff-47ee-b1bd-13ec50aa914f","colab":{"base_uri":"https://localhost:8080/","height":0},"outputId":"d6baf355-a6cc-4381-f85e-5189aef9d26d"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x500 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["import matplotlib.pyplot as plt\n","\n","# Data\n","data = timing_dict\n","\n","total_samples = len(dataset[\"train\"])\n","\n","batch_sizes = list(data.keys())\n","inference_times = list(data.values())\n","samples_per_second = [total_samples / time for bs, time in data.items()]\n","\n","# Create side-by-side plots\n","fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))\n","\n","# --- Left plot: Total Inference Time ---\n","ax1.bar([str(bs) for bs in batch_sizes], inference_times, color='steelblue')\n","ax1.set_xlabel('Batch Size')\n","ax1.set_ylabel('Total Inference Time (s)')\n","ax1.set_title('Inference Time by Batch Size')\n","\n","for i, v in enumerate(inference_times):\n","    ax1.text(i, v + 1, f'{v:.1f}', ha='center', fontsize=9)\n","\n","# --- ARROW LOGIC (Left) ---\n","# 1. Identify Start (Slowest) and End (Fastest)\n","start_val = max(inference_times)\n","end_val = min(inference_times)\n","start_idx = inference_times.index(start_val)\n","end_idx = inference_times.index(end_val)\n","\n","speedup = start_val / end_val\n","\n","# 2. Draw Arrow (No Text)\n","# connectionstyle \"rad=-0.3\" arcs the arrow upwards\n","ax1.annotate(\"\",\n","             xy=(end_idx, end_val+(0.5*end_val)),\n","             xytext=(start_idx+0.25, start_val+10),\n","             arrowprops=dict(arrowstyle=\"->\", color='green', lw=1.5, connectionstyle=\"arc3,rad=-0.3\"))\n","\n","# 3. Place Text at Midpoint\n","mid_x = (start_idx + end_idx) / 2\n","# Place text slightly above the highest point of the two bars\n","text_y = max(start_val, end_val) + (max(inference_times) * 0.1)\n","\n","ax1.text(mid_x+0.5, text_y-150, f\"{speedup:.1f}x speedup\",\n","         ha='center', va='bottom', fontweight='bold',\n","         bbox=dict(boxstyle=\"round,pad=0.3\", fc=\"white\", ec=\"none\", alpha=0.8))\n","\n","ax1.set_ylim(0, max(inference_times) * 1.35) # Increase headroom for text\n","\n","\n","# --- Right plot: Samples per Second ---\n","ax2.bar([str(bs) for bs in batch_sizes], samples_per_second, color='coral')\n","ax2.set_xlabel('Batch Size')\n","ax2.set_ylabel('Samples per Second')\n","ax2.set_title('Throughput by Batch Size')\n","\n","for i, v in enumerate(samples_per_second):\n","    ax2.text(i, v + 0.05, f'{v:.2f}', ha='center', fontsize=9)\n","\n","# --- ARROW LOGIC (Right) ---\n","# 1. Identify Start (Slowest) and End (Fastest)\n","start_val_t = min(samples_per_second)\n","end_val_t = max(samples_per_second)\n","start_idx_t = samples_per_second.index(start_val_t)\n","end_idx_t = samples_per_second.index(end_val_t)\n","\n","speedup_t = end_val_t / start_val_t\n","\n","# 2. Draw Arrow (No Text)\n","ax2.annotate(\"\",\n","             xy=(end_idx_t-(0.05*end_idx_t), end_val_t+(0.025*end_val_t)),\n","             xytext=(start_idx_t, start_val_t+0.6),\n","             arrowprops=dict(arrowstyle=\"->\", color='green', lw=1.5, connectionstyle=\"arc3,rad=-0.3\"))\n","\n","# 3. Place Text at Midpoint\n","mid_x_t = (start_idx_t + end_idx_t) / 2\n","text_y_t = max(start_val_t, end_val_t) + (max(samples_per_second) * 0.1)\n","\n","ax2.text(mid_x_t-0.5, text_y_t-4.5, f\"{speedup_t:.1f}x speedup\",\n","         ha='center', va='bottom', fontweight='bold',\n","         bbox=dict(boxstyle=\"round,pad=0.3\", fc=\"white\", ec=\"none\", alpha=0.8))\n","\n","ax2.set_ylim(0, max(samples_per_second) * 1.35) # Increase headroom\n","\n","plt.suptitle(\"Inference with Fine-Tuned Gemma 3 270M on GPU-T4\")\n","plt.tight_layout()\n","plt.savefig('inference_benchmark.png', dpi=150)\n","plt.show()"]},{"cell_type":"code","execution_count":44,"id":"7a6e07e9","metadata":{"executionInfo":{"elapsed":9,"status":"ok","timestamp":1769701977903,"user":{"displayName":"Tobias Nimo","userId":"18420403675581336337"},"user_tz":180},"id":"7a6e07e9","colab":{"base_uri":"https://localhost:8080/"},"outputId":"c7a10712-07d4-43db-f27b-7654b6f0b3ad"},"outputs":[{"output_type":"stream","name":"stdout","text":["[INFO] Number of samples per second: 1.8 | Number of samples per day: 155520.0\n"]}],"source":["samples_per_second = round(len(dataset[\"train\"]) / min(timing_dict.values()), 2)\n","seconds_in_a_day = 86_400\n","samples_per_day = seconds_in_a_day * samples_per_second\n","\n","print(f\"[INFO] Number of samples per second: {samples_per_second} | Number of samples per day: {samples_per_day}\")"]},{"cell_type":"markdown","id":"e0991404-74ac-4dc4-b80d-d04a883b0b94","metadata":{"id":"e0991404-74ac-4dc4-b80d-d04a883b0b94"},"source":["## [Bonus] Performing evaluations on our model\n","\n","We can evaluate our model directly against the original labels from `gpt-oss-120b` and see how it stacks up.\n","\n","1. Design metrics to compare ground truth with actual responses.\n","2. Collect samples which are different to the ground truth would allow us to further explore where our model needs improvements."]},{"cell_type":"markdown","id":"fe093b65","metadata":{"id":"fe093b65"},"source":["## Next steps\n","\n","There are several avenues we could approach next...\n","\n","1. Improve the data sampling - if our model makes mistakes, could we improve the input data? For example, more samples on certain tasks where the model is weaker.\n","\n","2. Quantisation - right now our model is ~500MB, could we make this smaller with the help of INT8 quantisation? (this could also help speed up inference time).\n","\n","3. Output compression - our output is compressed to mermaid-like format, but is there a better compression option to generate even less tokens?\n","\n","4. Test the model on a large unseen sample and randomly inspect them to find failure cases to boost the model performance.\n","\n","---"]}],"metadata":{"accelerator":"GPU","colab":{"collapsed_sections":["9u31JzuaWsmL","8dPVy9IjMDRq","CXW7Emt5UI6j","492095ab","629cb237","bf6784ab","1b744778","2efdc312-31c9-45cb-93f3-c9844d832b31","79c61ef3"],"gpuType":"T4","provenance":[{"file_id":"1hV6Gcz8vBRS9t0bYkBp6W1ne_yqG6mJx","timestamp":1769460680277},{"file_id":"https://github.com/mrdbourke/learn-huggingface/blob/main/notebooks/hugging_face_llm_full_fine_tune_tutorial.ipynb","timestamp":1767948638780}],"toc_visible":true},"kernelspec":{"display_name":"Python 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