{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# ROCO-idefics3-8b\n", "\n", "This notebook fine-tunes [Idefics3-8B-Llama3](https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3) model. The source model is fine-tuned on the [Radiology Objects in Context (ROCO)](https://huggingface.co/datasets/eltorio/ROCO-radiology) dataset, a large-scale medical and multimodal imaging collection. \n", "\n", "The fine-tuning process stores the model checkpoints on a regular basis. Re run the notebook from the last checkpoint to continue the fine-tuning process." ] }, { "cell_type": "markdown", "metadata": { "id": "2uGjdGkTI78H" }, "source": [ "## Try to mount Google Drive" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "executionInfo": { "elapsed": 2, "status": "aborted", "timestamp": 1730998196191, "user": { "displayName": "Ronan Le Meillat", "userId": "09161391957806824350" }, "user_tz": -60 }, "id": "F-zJG-uPIy3d" }, "outputs": [], "source": [ "try:\n", " import google.colab\n", " from google.colab import drive\n", " drive.mount('/content/drive')\n", " \n", "except ModuleNotFoundError:\n", " raise Exception(\"You are not running this code in Google Colab. Please use Google Colab if you would like to save the model to Google Drive\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fine-tuning parameters" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "executionInfo": { "elapsed": 1459, "status": "ok", "timestamp": 1730997027344, "user": { "displayName": "Ronan Le Meillat", "userId": "09161391957806824350" }, "user_tz": -60 }, "id": "8F3w0kcbAMtC" }, "outputs": [], "source": [ "dataset_id = \"eltorio/ROCO-radiology\"\n", "prompt= \"You are an expert radiologist certified with over 15 years of experience in diagnostic imaging, describe this image\"\n", "source_model_id = \"HuggingFaceM4/Idefics3-8B-Llama3\"\n", "destination_model_id = \"eltorio/ROCO-idefics3-8B\"\n", "# if Google Drive is mounted, the model will be saved in a folder called IDEFICS3_ROCO in the root of your Google Drive\n", "# else the model will be saved in the current working directory in a folder called IDEFICS3_ROCO\n", "if 'drive' in globals():\n", " output_dir = \"/content/drive/MyDrive/IDEFICS3_ROCO\"\n", "else:\n", " output_dir = \"IDEFICS3_ROCO\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Login on Hugging Face" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!git config --global credential.helper store\n", "%pip install huggingface_hub" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from huggingface_hub import login\n", "import os\n", "\n", "HF_TOKEN = \"\"\n", "\n", "if os.environ.get('HF_TOKEN') is not None:\n", " HF_TOKEN = os.environ.get('HF_TOKEN')\n", " print(f\"Hugging Face token found in environment variable\")\n", "try:\n", " import google.colab\n", " from google.colab import userdata\n", " if (userdata.get('HF_TOKEN') is not None) and (HF_TOKEN == \"\"):\n", " HF_TOKEN = userdata.get('HF_TOKEN')\n", " else:\n", " raise ValueError(\"Please set your Hugging Face token in the user data panel, or pass it as an environment variable\")\n", "except ModuleNotFoundError:\n", " if HF_TOKEN is None:\n", " raise ValueError(\"Please set your Hugging Face token in the user data panel, or pass it as an environment variable\")\n", "\n", "login(\n", " token=HF_TOKEN,\n", " add_to_git_credential=True\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Optionally clone the model repository" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# clone Hugging Face model repository\n", "!git clone https://huggingface.co/$destination_model_id $output_dir" ] }, { "cell_type": "markdown", "metadata": { "id": "s8QBFDGDAMtD" }, "source": [ "### Step 1: Install libraries and dependencies." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 33422, "status": "ok", "timestamp": 1730997063833, "user": { "displayName": "Ronan Le Meillat", "userId": "09161391957806824350" }, "user_tz": -60 }, "id": "eLNGOnQtAMtE", "outputId": "65d09e49-f24c-47f3-d7d7-c1a0a0237290" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n" ] } ], "source": [ "%pip install -q git+https://github.com/huggingface/transformers.git\n", "%pip install -q accelerate datasets peft\n", "%pip install -q bitsandbytes" ] }, { "cell_type": "markdown", "metadata": { "id": "IjLCnQVEAMtE" }, "source": [ "### Step 2: Retrieve the dataset from Hugging Face." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "9f1fb7edb868495b831e12c535c4ce77", "92e501753b0c49388ce317d0cbad704c", "5c0db4ce097c4a7d9235c48b71e83a76", 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"10248cbdaa7d4ea99830d3dae0838f0f", "c971e53f10b54a8faadb179df71597e7", "290b76e842e84e198c70c606fe20899e", "d974fa71d05148729e08d893095bad24", "669ee8f1b5fc408d81c2ddd2de5f6f9c", "08311eecb34641f5b602cc8a0888d86d", "82c64dba67694d889f0a0c01cf3830e7", "a6210ad7ceb247b0b136db6810605168", "1ae53773abea471580087a823e5301c1", "356430f29d3044acb0da3a7d12f611be", "a14307cb17164b57bdd09e7373681272", "17fe943fdb4546138856cececadbdc41", "cac374bd58e14b50bac063685ed741ab", "a9452c25f1664eb5a87d3e954f8768c8", "17e5bc7c84f846879478f68cc26a3603" ] }, "executionInfo": { "elapsed": 506620, "status": "ok", "timestamp": 1730997570445, "user": { "displayName": "Ronan Le Meillat", "userId": "09161391957806824350" }, "user_tz": -60 }, "id": "QBOsKy-jAMtF", "outputId": "4ccdbf0a-f96f-42e3-8825-6d63dc4a025a" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9f1fb7edb868495b831e12c535c4ce77", "version_major": 2, "version_minor": 0 }, "text/plain": [ "README.md: 0%| | 0.00/1.11k [00:00,\n", " 'image_id': 'RONA_00001',\n", " 'caption': 'Right shoulder of a 50-year-old patient showing an anterior dislocated shoulder.'}" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_dataset[len(train_dataset)-4]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "output_embedded_package_id": "1AWrHGFGw7ZieKf_KBSWS80zQilJUoVNj" }, "executionInfo": { "elapsed": 27473, "status": "ok", "timestamp": 1730997597913, "user": { "displayName": "Ronan Le Meillat", "userId": "09161391957806824350" }, "user_tz": -60 }, "id": "AIKCb2v3AMtF", "outputId": "12d78dcc-e318-4542-dd3a-7b7561d61fb5" }, "outputs": [ { "data": { "text/plain": [ "Output hidden; open in https://colab.research.google.com to view." ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "train_dataset[len(train_dataset)-4]['image']" ] }, { "cell_type": "markdown", "metadata": { "id": "Tu199rI3AMtG" }, "source": [ "### Step 4: Configure LoRA adapters" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 67, "referenced_widgets": [ "9f270a1faba44c1194d683a431d43f6f", "7403ea0ef7f04e2389c351b0e10a68ce", "fbc17ebe22854738b98bf2488c6105b1", "22e04cb812df4f5ca621b041c213d17e", "96d4f59ed68749eaaf9ad0c6a4e94f13", "7a8bc62ce92a4571ad42ac2beef851ef", "ae0117f952d94e469793bd8db3ad9a19", "0b8f8a93515d49488642b008e47b4bf3", "c9691e94467b4badbb71116136350c93", "fcd773b4e6154af78e7d476c59294e25", "7bd9e3fc12e64bc7bb95891dcd4153c7" ] }, "executionInfo": { "elapsed": 106706, "status": "ok", "timestamp": 1730998316179, "user": { "displayName": "Ronan Le Meillat", "userId": "09161391957806824350" }, "user_tz": -60 }, "id": "GTf6VxwwAMtG", "outputId": "86a93163-6b30-4e8b-ca1c-7392922a4aa6" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "`low_cpu_mem_usage` was None, now default to True since model is quantized.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9f270a1faba44c1194d683a431d43f6f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Loading checkpoint shards: 0%| | 0/4 [00:00\")\n", " ]\n", "\n", " def __call__(self, samples):\n", " texts = []\n", " images = []\n", " for sample in samples:\n", " image = sample[\"image\"]\n", " answer = sample[\"caption\"]\n", " messages = [\n", " {\n", " \"role\": \"system\",\n", " \"content\": [\n", " {\"type\": \"text\", \"text\": prompt}\n", " ]\n", "\n", " },\n", " {\n", " \"role\": \"user\",\n", " \"content\": [\n", " {\"type\": \"image\"},\n", " ]\n", " },\n", " {\n", " \"role\": \"assistant\",\n", " \"content\": [\n", " {\"type\": \"text\", \"text\": answer}\n", " ]\n", " }\n", " ]\n", " text = processor.apply_chat_template(messages, add_generation_prompt=False)\n", " texts.append(text.strip())\n", " images.append([image.convert('RGB')])\n", "\n", " batch = processor(text=texts, images=images, return_tensors=\"pt\", padding=True)\n", "\n", " labels = batch[\"input_ids\"].clone()\n", " labels[labels == processor.tokenizer.pad_token_id] = self.image_token_id\n", " batch[\"labels\"] = labels\n", "\n", " return batch\n", "\n", "data_collator = MyDataCollator(processor)" ] }, { "cell_type": "markdown", "metadata": { "id": "vsq4TtIJAMtH" }, "source": [ "### Step 6: Setup training parameters" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "executionInfo": { "elapsed": 1008, "status": "ok", "timestamp": 1730998601172, "user": { "displayName": "Ronan Le Meillat", "userId": "09161391957806824350" }, "user_tz": -60 }, "id": "Q_WKQFfoAMtH" }, "outputs": [], "source": [ "from transformers import TrainingArguments, Trainer\n", "\n", "training_args = TrainingArguments(\n", " output_dir = output_dir,\n", " overwrite_output_dir = False,\n", " auto_find_batch_size = True,\n", " learning_rate = 2e-4,\n", " fp16 = True,\n", " per_device_train_batch_size = 2,\n", " per_device_eval_batch_size = 2,\n", " gradient_accumulation_steps = 8,\n", " dataloader_pin_memory = False,\n", " save_total_limit = 3,\n", " evaluation_strategy = None,\n", " save_strategy = \"steps\",\n", " eval_steps = 100,\n", " save_steps = 10, # checkpoint each 10 steps\n", " resume_from_checkpoint = True,\n", " logging_steps = 5,\n", " remove_unused_columns = False,\n", " push_to_hub = True,\n", " label_names = [\"labels\"],\n", " load_best_model_at_end = False,\n", " report_to = \"none\",\n", " optim = \"paged_adamw_8bit\",\n", ")" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 426, "status": "ok", "timestamp": 1730998605441, "user": { "displayName": "Ronan Le Meillat", "userId": "09161391957806824350" }, "user_tz": -60 }, "id": "vSIo17mgAMtH", "outputId": "3bebd35a-ed7f-49ee-e1bc-91594e8dcd24" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py:494: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n", " self.scaler = torch.cuda.amp.GradScaler(**kwargs)\n" ] } ], "source": [ "trainer = Trainer(\n", " model = model,\n", " args = training_args,\n", " data_collator = data_collator,\n", " train_dataset = train_dataset,\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "pmlwDsOpAMtI" }, "source": [ "### Step 7: Start (or restart) Training" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "WQA84KnTAMtI", "outputId": "ebb15160-f56e-4899-e608-b0d5fd0ba117" }, "outputs": [ { "data": { "text/html": [ "\n", "
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StepTraining Loss

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