{ "cells": [ { "cell_type": "code", "execution_count": 3, "id": "38837f4a", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "282526cb378b487a8b6743dcdbaaf51d", "version_major": 2, "version_minor": 0 }, "text/plain": [ "NuExtract3-Q4_K_M.gguf: 0%| | 0.00/2.71G [00:00, ). See the original errors:\n\nwhile loading with AutoModelForImageTextToText, an error is thrown:\nTraceback (most recent call last):\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\pipelines\\base.py\", line 240, in load_model\n model = model_class.from_pretrained(model, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\models\\auto\\auto_factory.py\", line 336, in from_pretrained\n config, kwargs = AutoConfig.from_pretrained(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\models\\auto\\configuration_auto.py\", line 376, in from_pretrained\n config_dict, unused_kwargs = PreTrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\configuration_utils.py\", line 721, in get_config_dict\n config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\configuration_utils.py\", line 807, in _get_config_dict\n config_dict = load_gguf_checkpoint(resolved_config_file, return_tensors=False)[\"config\"]\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\modeling_gguf_pytorch_utils.py\", line 590, in load_gguf_checkpoint\n if is_gguf_available() and is_torch_available():\n ^^^^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\utils\\import_utils.py\", line 1207, in is_gguf_available\n return is_available and version.parse(gguf_version) >= version.parse(min_version)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\packaging\\version.py\", line 56, in parse\n return Version(version)\n ^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\packaging\\version.py\", line 202, in __init__\n raise InvalidVersion(f\"Invalid version: '{version}'\")\npackaging.version.InvalidVersion: Invalid version: 'N/A'\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\pipelines\\base.py\", line 256, in load_model\n model = model_class.from_pretrained(model, **fp32_kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\models\\auto\\auto_factory.py\", line 336, in from_pretrained\n config, kwargs = AutoConfig.from_pretrained(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\models\\auto\\configuration_auto.py\", line 376, in from_pretrained\n config_dict, unused_kwargs = PreTrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\configuration_utils.py\", line 721, in get_config_dict\n config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\configuration_utils.py\", line 807, in _get_config_dict\n config_dict = load_gguf_checkpoint(resolved_config_file, return_tensors=False)[\"config\"]\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\modeling_gguf_pytorch_utils.py\", line 590, in load_gguf_checkpoint\n if is_gguf_available() and is_torch_available():\n ^^^^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\utils\\import_utils.py\", line 1207, in is_gguf_available\n return is_available and version.parse(gguf_version) >= version.parse(min_version)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\packaging\\version.py\", line 56, in parse\n return Version(version)\n ^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\packaging\\version.py\", line 202, in __init__\n raise InvalidVersion(f\"Invalid version: '{version}'\")\npackaging.version.InvalidVersion: Invalid version: 'N/A'\n\nwhile loading with Qwen3_5ForConditionalGeneration, an error is thrown:\nTraceback (most recent call last):\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\pipelines\\base.py\", line 240, in load_model\n model = model_class.from_pretrained(model, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\modeling_utils.py\", line 4281, in from_pretrained\n raise ValueError(\"accelerate is required when loading a GGUF file `pip install accelerate`.\")\nValueError: accelerate is required when loading a GGUF file `pip install accelerate`.\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\pipelines\\base.py\", line 256, in load_model\n model = model_class.from_pretrained(model, **fp32_kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\modeling_utils.py\", line 4281, in from_pretrained\n raise ValueError(\"accelerate is required when loading a GGUF file `pip install accelerate`.\")\nValueError: accelerate is required when loading a GGUF file `pip install accelerate`.\n\n\n", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m 2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m transformers \u001b[38;5;28;01mimport\u001b[39;00m AutoConfig, AutoProcessor, pipeline\n\u001b[32m 3\u001b[39m \n\u001b[32m 4\u001b[39m config = AutoConfig.from_pretrained(\u001b[33m\"numind/NuExtract3\"\u001b[39m)\n\u001b[32m 5\u001b[39m processor = AutoProcessor.from_pretrained(\u001b[33m\"numind/NuExtract3\"\u001b[39m)\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m pipe = pipeline(\u001b[33m\"image-text-to-text\"\u001b[39m, model=\u001b[33m\"numind/NuExtract3-GGUF\"\u001b[39m, config=config, processor=processor, device_map=\u001b[33m\"auto\"\u001b[39m, model_kwargs={\u001b[33m\"gguf_file\"\u001b[39m: \u001b[33m\"NuExtract3-Q4_K_M.gguf\"\u001b[39m})\n\u001b[32m 7\u001b[39m image = str(next((Path.cwd() / \u001b[33m\"data\"\u001b[39m / \u001b[33m\"samples\"\u001b[39m).glob(\u001b[33m\"*.png\"\u001b[39m)))\n\u001b[32m 8\u001b[39m messages = [{\"role\": \"user\", \"content\": [\n\u001b[32m 9\u001b[39m {\u001b[33m\"type\"\u001b[39m: \u001b[33m\"image\"\u001b[39m, \u001b[33m\"url\"\u001b[39m: image},\n", "\u001b[36mFile \u001b[39m\u001b[32ms:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\pipelines\\__init__.py:1033\u001b[39m, in \u001b[36mpipeline\u001b[39m\u001b[34m(task, model, config, tokenizer, feature_extractor, image_processor, video_processor, processor, revision, use_fast, token, device, device_map, dtype, trust_remote_code, model_kwargs, pipeline_class, **kwargs)\u001b[39m\n\u001b[32m 1031\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(model, \u001b[38;5;28mstr\u001b[39m):\n\u001b[32m 1032\u001b[39m model_classes = targeted_task[\u001b[33m\"\u001b[39m\u001b[33mpt\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m-> \u001b[39m\u001b[32m1033\u001b[39m model = \u001b[30;43mload_model\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m 1034\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43madapter_path\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mif\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43madapter_path\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mis\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mnot\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mNone\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43;01melse\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mmodel\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1035\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mmodel_classes\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mmodel_classes\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1036\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mconfig\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mconfig\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1037\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43mtask\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtask\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1038\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mhub_kwargs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1039\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mmodel_kwargs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m 1040\u001b[39m \u001b[30;43m \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 1042\u001b[39m hub_kwargs[\u001b[33m\"\u001b[39m\u001b[33m_commit_hash\u001b[39m\u001b[33m\"\u001b[39m] = model.config._commit_hash\n\u001b[32m 1044\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m pipeline_class \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", "\u001b[36mFile \u001b[39m\u001b[32ms:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\pipelines\\base.py:276\u001b[39m, in \u001b[36mload_model\u001b[39m\u001b[34m(model, config, model_classes, task, **model_kwargs)\u001b[39m\n\u001b[32m 274\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m class_name, trace \u001b[38;5;129;01min\u001b[39;00m all_traceback.items():\n\u001b[32m 275\u001b[39m error += \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mwhile loading with \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mclass_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m, an error is thrown:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mtrace\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m--> \u001b[39m\u001b[32m276\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m 277\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mCould not load model \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodel\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m with any of the following classes: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mclass_tuple\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m. See the original errors:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00merror\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 278\u001b[39m )\n\u001b[32m 280\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m model\n", "\u001b[31mValueError\u001b[39m: Could not load model numind/NuExtract3-GGUF with any of the following classes: (, ). See the original errors:\n\nwhile loading with AutoModelForImageTextToText, an error is thrown:\nTraceback (most recent call last):\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\pipelines\\base.py\", line 240, in load_model\n model = model_class.from_pretrained(model, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\models\\auto\\auto_factory.py\", line 336, in from_pretrained\n config, kwargs = AutoConfig.from_pretrained(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\models\\auto\\configuration_auto.py\", line 376, in from_pretrained\n config_dict, unused_kwargs = PreTrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\configuration_utils.py\", line 721, in get_config_dict\n config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\configuration_utils.py\", line 807, in _get_config_dict\n config_dict = load_gguf_checkpoint(resolved_config_file, return_tensors=False)[\"config\"]\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\modeling_gguf_pytorch_utils.py\", line 590, in load_gguf_checkpoint\n if is_gguf_available() and is_torch_available():\n ^^^^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\utils\\import_utils.py\", line 1207, in is_gguf_available\n return is_available and version.parse(gguf_version) >= version.parse(min_version)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\packaging\\version.py\", line 56, in parse\n return Version(version)\n ^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\packaging\\version.py\", line 202, in __init__\n raise InvalidVersion(f\"Invalid version: '{version}'\")\npackaging.version.InvalidVersion: Invalid version: 'N/A'\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\pipelines\\base.py\", line 256, in load_model\n model = model_class.from_pretrained(model, **fp32_kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\models\\auto\\auto_factory.py\", line 336, in from_pretrained\n config, kwargs = AutoConfig.from_pretrained(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\models\\auto\\configuration_auto.py\", line 376, in from_pretrained\n config_dict, unused_kwargs = PreTrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\configuration_utils.py\", line 721, in get_config_dict\n config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\configuration_utils.py\", line 807, in _get_config_dict\n config_dict = load_gguf_checkpoint(resolved_config_file, return_tensors=False)[\"config\"]\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\modeling_gguf_pytorch_utils.py\", line 590, in load_gguf_checkpoint\n if is_gguf_available() and is_torch_available():\n ^^^^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\utils\\import_utils.py\", line 1207, in is_gguf_available\n return is_available and version.parse(gguf_version) >= version.parse(min_version)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\packaging\\version.py\", line 56, in parse\n return Version(version)\n ^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\packaging\\version.py\", line 202, in __init__\n raise InvalidVersion(f\"Invalid version: '{version}'\")\npackaging.version.InvalidVersion: Invalid version: 'N/A'\n\nwhile loading with Qwen3_5ForConditionalGeneration, an error is thrown:\nTraceback (most recent call last):\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\pipelines\\base.py\", line 240, in load_model\n model = model_class.from_pretrained(model, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\modeling_utils.py\", line 4281, in from_pretrained\n raise ValueError(\"accelerate is required when loading a GGUF file `pip install accelerate`.\")\nValueError: accelerate is required when loading a GGUF file `pip install accelerate`.\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\pipelines\\base.py\", line 256, in load_model\n model = model_class.from_pretrained(model, **fp32_kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"s:\\Spaces\\Data-Extraction\\NuMarkApp\\.venv\\Lib\\site-packages\\transformers\\modeling_utils.py\", line 4281, in from_pretrained\n raise ValueError(\"accelerate is required when loading a GGUF file `pip install accelerate`.\")\nValueError: accelerate is required when loading a GGUF file `pip install accelerate`.\n\n\n" ] } ], "source": [ "from pathlib import Path\n", "from transformers import AutoConfig, AutoProcessor, pipeline\n", "\n", "config = AutoConfig.from_pretrained(\"numind/NuExtract3\")\n", "processor = AutoProcessor.from_pretrained(\"numind/NuExtract3\")\n", "pipe = pipeline(\"image-text-to-text\", model=\"numind/NuExtract3-GGUF\", config=config, processor=processor, device_map=\"auto\", model_kwargs={\"gguf_file\": \"NuExtract3-Q4_K_M.gguf\"})\n", "image = str(next((Path.cwd() / \"data\" / \"samples\").glob(\"*.png\")))\n", "messages = [{\"role\": \"user\", \"content\": [\n", " {\"type\": \"image\", \"url\": image},\n", " {\"type\": \"text\", \"text\": \"Extract all document content as structured JSON.\"},\n", "]}]\n", "pipe(text=messages, max_new_tokens=4096)" ] } ], "metadata": { "kernelspec": { "display_name": ".venv", "language": "python", "name": "python3" }, "language_info": { "name": "python", "version": "3.12.10" } }, "nbformat": 4, "nbformat_minor": 5 }