diff --git "a/ROCO-idefics3.ipynb" "b/ROCO-idefics3.ipynb" new file mode 100644--- /dev/null +++ "b/ROCO-idefics3.ipynb" @@ -0,0 +1,14799 @@ +{ + "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", + "if os.getenv('HF_TOKEN') is not None:\n", + " HF_TOKEN = os.getenv('HF_TOKEN')\n", + " print(f\"Hugging Face token found in environment variable\")\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", + "6b491ec2f7604976b347a5feb66a2a2d", + "98e545ecb77b461290d24e5ccd3cc1d5", + "18908dfa00534f9b99fd5973c070bbdc", + "ee551beb730743cc8fcf98c8d467de22", + "38b31d54ad634e19b41544ba6fa182d6", + "e6600d6b8cfe429480cbdb2b993c1aa6", + "69d0bffcb7eb4a0d95c743a99dd06725", + "a784b176de684e0a8be7995f16212a3a", + "1bd6753e9e69481992048d1eafe5849e", + "3e3859d3e3c240119a0eb791e611494d", + "af2693b9442e4c4896396db4d8336ea5", + "031dd614106946d79cd0dfe67cefa023", + "5f2589177ecd47908828ac2b19a1d0df", + 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"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
54.297500
100.426300
150.401400
200.339700
250.357200
300.362800
350.309600
400.335300
450.373300
500.295800
550.355300
600.309700
650.321900
700.344400
750.360000
800.330500
850.292400
900.289300
950.330900
1000.294200
1050.266200
1100.287000
1150.314700
1200.337800
1250.356300
1300.316500
1350.244500
1400.306700
1450.359400
1500.295700
1550.286800
1600.320300
1650.315400
1700.315800
1750.332500
1800.284600
1850.311800
1900.312600
1950.279400
2000.295700
2050.295900
2100.309700
2150.316500
2200.324500
2250.266900
2300.312500
2350.381600
2400.283900
2450.281300
2500.330500
2550.348600
2600.297100
2650.282000
2700.274300
2750.302300
2800.246200
2850.290800
2900.310900
2950.324900
3000.318600
3050.297300
3100.324000
3150.299600
3200.275900
3250.310400
3300.296800
3350.296600
3400.273500
3450.281300
3500.347000
3550.295400
3600.310700
3650.298200
3700.284500
3750.281000
3800.315900
3850.275900
3900.361100
3950.314700
4000.296900
4050.316800
4100.298100
4150.294500
4200.370400
4250.290500
4300.360600
4350.338200
4400.289300
4450.305600
4500.348300
4550.297100
4600.289700
4650.309300
4700.252200
4750.318000
4800.287100
4850.299000
4900.321500
4950.330100
5000.298800
5050.334100
5100.328800
5150.307400
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