Spaces:
Running on Zero
Running on Zero
File size: 21,226 Bytes
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"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<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"ename": "ValueError",
"evalue": "Could not load model numind/NuExtract3-GGUF with any of the following classes: (<class 'transformers.models.auto.modeling_auto.AutoModelForImageTextToText'>, <class 'transformers.models.qwen3_5.modeling_qwen3_5.Qwen3_5ForConditionalGeneration'>). 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: (<class 'transformers.models.auto.modeling_auto.AutoModelForImageTextToText'>, <class 'transformers.models.qwen3_5.modeling_qwen3_5.Qwen3_5ForConditionalGeneration'>). 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
}
|