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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "50ddeec2e802423ebf210b0264d2f222",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading checkpoint shards: 0%| | 0/4 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/anaconda3/lib/python3.12/site-packages/bitsandbytes/cextension.py:34: UserWarning: The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable.\n",
" warn(\"The installed version of bitsandbytes was compiled without GPU support. \"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"'NoneType' object has no attribute 'cadam32bit_grad_fp32'\n"
]
}
],
"source": [
"\n",
"from transformers import AutoProcessor, Idefics3ForConditionalGeneration, image_utils\n",
"import torch\n",
"device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
"model_id=\"eltorio/IDEFICS3_ROCO\"\n",
"# model = AutoModelForImageTextToText.from_pretrained(model_id).to(device)\n",
"base_model_path=\"HuggingFaceM4/Idefics3-8B-Llama3\" #or change to local path\n",
"processor = AutoProcessor.from_pretrained(base_model_path)\n",
"model = Idefics3ForConditionalGeneration.from_pretrained(\n",
" base_model_path, torch_dtype=torch.bfloat16\n",
" ).to(device)\n",
"\n",
"model.load_adapter(model_id)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from transformers import image_utils\n",
"image = image_utils.load_image('https://github.com/sctg-development/ROCOv2-radiology/blob/main/source_dataset/test/ROCOv2_2023_test_000005.jpg?raw=true')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"messages = [\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": [\n",
" {\"type\": \"image\"},\n",
" {\"type\": \"text\", \"text\": \"What do we see in this image?\"},\n",
" ]\n",
" },\n",
"]\n",
"prompt = processor.apply_chat_template(messages, add_generation_prompt=True)\n",
"inputs = processor(text=prompt, images=[image], return_tensors=\"pt\")\n",
"inputs = {k: v.to(device) for k, v in inputs.items()}"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['User:<image>What do we see in this image?\\nAssistant: Buccal and palatal cortical bone thickness measurements.\\n']\n"
]
}
],
"source": [
"# Generate\n",
"generated_ids = model.generate(**inputs, max_new_tokens=500)\n",
"generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)\n",
"\n",
"print(generated_texts)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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