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CodeLlama-7b-Text-to-SQL-mps-finetuned

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code
lora
nlp
mps
conversational
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+ {
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+ "cells": [
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+ {
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+ "cell_type": "code",
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+ "execution_count": 1,
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+ "id": "95945cf4",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "# Cell 1: 安装依赖 (如果还没装)\n",
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+ "# !pip install -q transformers torch accelerate"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 1,
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+ "id": "063e56f2",
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "🚀 环境已重置，使用设备: mps\n",
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+ "⏳ 正在加载模型 (MPS + Float16)...\n"
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+ ]
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+ },
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ "python(51576) MallocStackLogging: can't turn off malloc stack logging because it was not enabled.\n"
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+ ]
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+ },
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+ {
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+ "data": {
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+ "application/vnd.jupyter.widget-view+json": {
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+ "model_id": "4f71a457fb0f4f7dbcfd0f002224ffb8",
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+ "version_major": 2,
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+ "version_minor": 0
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+ },
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+ "text/plain": [
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+ "Downloading shards: 0%| | 0/3 [00:00<?, ?it/s]"
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+ ]
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+ },
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+ "metadata": {},
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+ "output_type": "display_data"
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+ },
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+ {
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+ "data": {
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+ "application/vnd.jupyter.widget-view+json": {
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+ "model_id": "59f84747f1744f7dbea42305928cc554",
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+ "version_major": 2,
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+ "version_minor": 0
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+ },
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+ "text/plain": [
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+ "model-00001-of-00003.safetensors: 20%|## | 1.01G/4.94G [00:00<?, ?B/s]"
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+ ]
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+ },
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+ "metadata": {},
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+ "output_type": "display_data"
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+ },
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+ {
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+ "ename": "KeyboardInterrupt",
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+ "evalue": "",
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+ "output_type": "error",
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+ "traceback": [
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+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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+ "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
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+ "Cell \u001b[0;32mIn[1], line 19\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m⏳ 正在加载模型 (MPS + Float16)...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 18\u001b[0m \u001b[38;5;66;03m# 加载 Base Model\u001b[39;00m\n\u001b[0;32m---> 19\u001b[0m model \u001b[38;5;241m=\u001b[39m AutoModelForCausalLM\u001b[38;5;241m.\u001b[39mfrom_pretrained(\n\u001b[1;32m 20\u001b[0m model_id,\n\u001b[1;32m 21\u001b[0m device_map\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m: device}, \n\u001b[1;32m 22\u001b[0m torch_dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mfloat16, \u001b[38;5;66;03m# 半精度，省显存\u001b[39;00m\n\u001b[1;32m 23\u001b[0m )\n\u001b[1;32m 26\u001b[0m model\u001b[38;5;241m.\u001b[39meval()\n\u001b[1;32m 28\u001b[0m \u001b[38;5;66;03m# 加载 Tokenizer\u001b[39;00m\n",
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+ "File \u001b[0;32m~/miniconda3/envs/py312/lib/python3.12/site-packages/transformers/models/auto/auto_factory.py:564\u001b[0m, in \u001b[0;36m_BaseAutoModelClass.from_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m 562\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(config) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_model_mapping\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[1;32m 563\u001b[0m model_class \u001b[38;5;241m=\u001b[39m _get_model_class(config, \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_model_mapping)\n\u001b[0;32m--> 564\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m model_class\u001b[38;5;241m.\u001b[39mfrom_pretrained(\n\u001b[1;32m 565\u001b[0m pretrained_model_name_or_path, \u001b[38;5;241m*\u001b[39mmodel_args, config\u001b[38;5;241m=\u001b[39mconfig, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mhub_kwargs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[1;32m 566\u001b[0m )\n\u001b[1;32m 567\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 568\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnrecognized configuration class \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconfig\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m for this kind of AutoModel: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 569\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mModel type should be one of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(c\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mfor\u001b[39;00m\u001b[38;5;250m \u001b[39mc\u001b[38;5;250m \u001b[39m\u001b[38;5;129;01min\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_model_mapping\u001b[38;5;241m.\u001b[39mkeys())\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 570\u001b[0m )\n",
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+ "File \u001b[0;32m~/miniconda3/envs/py312/lib/python3.12/site-packages/transformers/modeling_utils.py:3944\u001b[0m, in \u001b[0;36mPreTrainedModel.from_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, weights_only, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m 3941\u001b[0m \u001b[38;5;66;03m# We'll need to download and cache each checkpoint shard if the checkpoint is sharded.\u001b[39;00m\n\u001b[1;32m 3942\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_sharded:\n\u001b[1;32m 3943\u001b[0m \u001b[38;5;66;03m# resolved_archive_file becomes a list of files that point to the different checkpoint shards in this case.\u001b[39;00m\n\u001b[0;32m-> 3944\u001b[0m resolved_archive_file, sharded_metadata \u001b[38;5;241m=\u001b[39m get_checkpoint_shard_files(\n\u001b[1;32m 3945\u001b[0m pretrained_model_name_or_path,\n\u001b[1;32m 3946\u001b[0m resolved_archive_file,\n\u001b[1;32m 3947\u001b[0m cache_dir\u001b[38;5;241m=\u001b[39mcache_dir,\n\u001b[1;32m 3948\u001b[0m force_download\u001b[38;5;241m=\u001b[39mforce_download,\n\u001b[1;32m 3949\u001b[0m proxies\u001b[38;5;241m=\u001b[39mproxies,\n\u001b[1;32m 3950\u001b[0m resume_download\u001b[38;5;241m=\u001b[39mresume_download,\n\u001b[1;32m 3951\u001b[0m local_files_only\u001b[38;5;241m=\u001b[39mlocal_files_only,\n\u001b[1;32m 3952\u001b[0m token\u001b[38;5;241m=\u001b[39mtoken,\n\u001b[1;32m 3953\u001b[0m user_agent\u001b[38;5;241m=\u001b[39muser_agent,\n\u001b[1;32m 3954\u001b[0m revision\u001b[38;5;241m=\u001b[39mrevision,\n\u001b[1;32m 3955\u001b[0m subfolder\u001b[38;5;241m=\u001b[39msubfolder,\n\u001b[1;32m 3956\u001b[0m _commit_hash\u001b[38;5;241m=\u001b[39mcommit_hash,\n\u001b[1;32m 3957\u001b[0m )\n\u001b[1;32m 3959\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 3960\u001b[0m is_safetensors_available()\n\u001b[1;32m 3961\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(resolved_archive_file, \u001b[38;5;28mstr\u001b[39m)\n\u001b[1;32m 3962\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m resolved_archive_file\u001b[38;5;241m.\u001b[39mendswith(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.safetensors\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 3963\u001b[0m ):\n\u001b[1;32m 3964\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m safe_open(resolved_archive_file, framework\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n",
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+ "File \u001b[0;32m~/miniconda3/envs/py312/lib/python3.12/site-packages/transformers/utils/hub.py:1098\u001b[0m, in \u001b[0;36mget_checkpoint_shard_files\u001b[0;34m(pretrained_model_name_or_path, index_filename, cache_dir, force_download, proxies, resume_download, local_files_only, token, user_agent, revision, subfolder, _commit_hash, **deprecated_kwargs)\u001b[0m\n\u001b[1;32m 1095\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m shard_filename \u001b[38;5;129;01min\u001b[39;00m tqdm(shard_filenames, desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDownloading shards\u001b[39m\u001b[38;5;124m\"\u001b[39m, disable\u001b[38;5;241m=\u001b[39m\u001b[38;5;129;01mnot\u001b[39;00m show_progress_bar):\n\u001b[1;32m 1096\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1097\u001b[0m \u001b[38;5;66;03m# Load from URL\u001b[39;00m\n\u001b[0;32m-> 1098\u001b[0m cached_filename \u001b[38;5;241m=\u001b[39m cached_file(\n\u001b[1;32m 1099\u001b[0m pretrained_model_name_or_path,\n\u001b[1;32m 1100\u001b[0m shard_filename,\n\u001b[1;32m 1101\u001b[0m cache_dir\u001b[38;5;241m=\u001b[39mcache_dir,\n\u001b[1;32m 1102\u001b[0m force_download\u001b[38;5;241m=\u001b[39mforce_download,\n\u001b[1;32m 1103\u001b[0m proxies\u001b[38;5;241m=\u001b[39mproxies,\n\u001b[1;32m 1104\u001b[0m resume_download\u001b[38;5;241m=\u001b[39mresume_download,\n\u001b[1;32m 1105\u001b[0m local_files_only\u001b[38;5;241m=\u001b[39mlocal_files_only,\n\u001b[1;32m 1106\u001b[0m token\u001b[38;5;241m=\u001b[39mtoken,\n\u001b[1;32m 1107\u001b[0m user_agent\u001b[38;5;241m=\u001b[39muser_agent,\n\u001b[1;32m 1108\u001b[0m revision\u001b[38;5;241m=\u001b[39mrevision,\n\u001b[1;32m 1109\u001b[0m subfolder\u001b[38;5;241m=\u001b[39msubfolder,\n\u001b[1;32m 1110\u001b[0m _commit_hash\u001b[38;5;241m=\u001b[39m_commit_hash,\n\u001b[1;32m 1111\u001b[0m )\n\u001b[1;32m 1112\u001b[0m \u001b[38;5;66;03m# We have already dealt with RepositoryNotFoundError and RevisionNotFoundError when getting the index, so\u001b[39;00m\n\u001b[1;32m 1113\u001b[0m \u001b[38;5;66;03m# we don't have to catch them here.\u001b[39;00m\n\u001b[1;32m 1114\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m EntryNotFoundError:\n",
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+ "File \u001b[0;32m~/miniconda3/envs/py312/lib/python3.12/site-packages/transformers/utils/hub.py:403\u001b[0m, in \u001b[0;36mcached_file\u001b[0;34m(path_or_repo_id, filename, cache_dir, force_download, resume_download, proxies, token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_gated_repo, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash, **deprecated_kwargs)\u001b[0m\n\u001b[1;32m 400\u001b[0m user_agent \u001b[38;5;241m=\u001b[39m http_user_agent(user_agent)\n\u001b[1;32m 401\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 402\u001b[0m \u001b[38;5;66;03m# Load from URL or cache if already cached\u001b[39;00m\n\u001b[0;32m--> 403\u001b[0m resolved_file \u001b[38;5;241m=\u001b[39m hf_hub_download(\n\u001b[1;32m 404\u001b[0m path_or_repo_id,\n\u001b[1;32m 405\u001b[0m filename,\n\u001b[1;32m 406\u001b[0m subfolder\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(subfolder) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m subfolder,\n\u001b[1;32m 407\u001b[0m repo_type\u001b[38;5;241m=\u001b[39mrepo_type,\n\u001b[1;32m 408\u001b[0m revision\u001b[38;5;241m=\u001b[39mrevision,\n\u001b[1;32m 409\u001b[0m cache_dir\u001b[38;5;241m=\u001b[39mcache_dir,\n\u001b[1;32m 410\u001b[0m user_agent\u001b[38;5;241m=\u001b[39muser_agent,\n\u001b[1;32m 411\u001b[0m force_download\u001b[38;5;241m=\u001b[39mforce_download,\n\u001b[1;32m 412\u001b[0m proxies\u001b[38;5;241m=\u001b[39mproxies,\n\u001b[1;32m 413\u001b[0m resume_download\u001b[38;5;241m=\u001b[39mresume_download,\n\u001b[1;32m 414\u001b[0m token\u001b[38;5;241m=\u001b[39mtoken,\n\u001b[1;32m 415\u001b[0m local_files_only\u001b[38;5;241m=\u001b[39mlocal_files_only,\n\u001b[1;32m 416\u001b[0m )\n\u001b[1;32m 417\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m GatedRepoError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 418\u001b[0m resolved_file \u001b[38;5;241m=\u001b[39m _get_cache_file_to_return(path_or_repo_id, full_filename, cache_dir, revision)\n",
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+ "File \u001b[0;32m~/miniconda3/envs/py312/lib/python3.12/site-packages/huggingface_hub-0.27.1-py3.8.egg/huggingface_hub/utils/_validators.py:114\u001b[0m, in \u001b[0;36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m check_use_auth_token:\n\u001b[1;32m 112\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m smoothly_deprecate_use_auth_token(fn_name\u001b[38;5;241m=\u001b[39mfn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, has_token\u001b[38;5;241m=\u001b[39mhas_token, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[0;32m--> 114\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
76
+ "File \u001b[0;32m~/miniconda3/envs/py312/lib/python3.12/site-packages/huggingface_hub-0.27.1-py3.8.egg/huggingface_hub/file_download.py:860\u001b[0m, in \u001b[0;36mhf_hub_download\u001b[0;34m(repo_id, filename, subfolder, repo_type, revision, library_name, library_version, cache_dir, local_dir, user_agent, force_download, proxies, etag_timeout, token, local_files_only, headers, endpoint, resume_download, force_filename, local_dir_use_symlinks)\u001b[0m\n\u001b[1;32m 840\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _hf_hub_download_to_local_dir(\n\u001b[1;32m 841\u001b[0m \u001b[38;5;66;03m# Destination\u001b[39;00m\n\u001b[1;32m 842\u001b[0m local_dir\u001b[38;5;241m=\u001b[39mlocal_dir,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 857\u001b[0m local_files_only\u001b[38;5;241m=\u001b[39mlocal_files_only,\n\u001b[1;32m 858\u001b[0m )\n\u001b[1;32m 859\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 860\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _hf_hub_download_to_cache_dir(\n\u001b[1;32m 861\u001b[0m \u001b[38;5;66;03m# Destination\u001b[39;00m\n\u001b[1;32m 862\u001b[0m cache_dir\u001b[38;5;241m=\u001b[39mcache_dir,\n\u001b[1;32m 863\u001b[0m \u001b[38;5;66;03m# File info\u001b[39;00m\n\u001b[1;32m 864\u001b[0m repo_id\u001b[38;5;241m=\u001b[39mrepo_id,\n\u001b[1;32m 865\u001b[0m filename\u001b[38;5;241m=\u001b[39mfilename,\n\u001b[1;32m 866\u001b[0m repo_type\u001b[38;5;241m=\u001b[39mrepo_type,\n\u001b[1;32m 867\u001b[0m revision\u001b[38;5;241m=\u001b[39mrevision,\n\u001b[1;32m 868\u001b[0m \u001b[38;5;66;03m# HTTP info\u001b[39;00m\n\u001b[1;32m 869\u001b[0m endpoint\u001b[38;5;241m=\u001b[39mendpoint,\n\u001b[1;32m 870\u001b[0m etag_timeout\u001b[38;5;241m=\u001b[39metag_timeout,\n\u001b[1;32m 871\u001b[0m headers\u001b[38;5;241m=\u001b[39mhf_headers,\n\u001b[1;32m 872\u001b[0m proxies\u001b[38;5;241m=\u001b[39mproxies,\n\u001b[1;32m 873\u001b[0m token\u001b[38;5;241m=\u001b[39mtoken,\n\u001b[1;32m 874\u001b[0m \u001b[38;5;66;03m# Additional options\u001b[39;00m\n\u001b[1;32m 875\u001b[0m local_files_only\u001b[38;5;241m=\u001b[39mlocal_files_only,\n\u001b[1;32m 876\u001b[0m force_download\u001b[38;5;241m=\u001b[39mforce_download,\n\u001b[1;32m 877\u001b[0m )\n",
77
+ "File \u001b[0;32m~/miniconda3/envs/py312/lib/python3.12/site-packages/huggingface_hub-0.27.1-py3.8.egg/huggingface_hub/file_download.py:1009\u001b[0m, in \u001b[0;36m_hf_hub_download_to_cache_dir\u001b[0;34m(cache_dir, repo_id, filename, repo_type, revision, endpoint, etag_timeout, headers, proxies, token, local_files_only, force_download)\u001b[0m\n\u001b[1;32m 1007\u001b[0m Path(lock_path)\u001b[38;5;241m.\u001b[39mparent\u001b[38;5;241m.\u001b[39mmkdir(parents\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, exist_ok\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 1008\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m WeakFileLock(lock_path):\n\u001b[0;32m-> 1009\u001b[0m _download_to_tmp_and_move(\n\u001b[1;32m 1010\u001b[0m incomplete_path\u001b[38;5;241m=\u001b[39mPath(blob_path \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.incomplete\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m 1011\u001b[0m destination_path\u001b[38;5;241m=\u001b[39mPath(blob_path),\n\u001b[1;32m 1012\u001b[0m url_to_download\u001b[38;5;241m=\u001b[39murl_to_download,\n\u001b[1;32m 1013\u001b[0m proxies\u001b[38;5;241m=\u001b[39mproxies,\n\u001b[1;32m 1014\u001b[0m headers\u001b[38;5;241m=\u001b[39mheaders,\n\u001b[1;32m 1015\u001b[0m expected_size\u001b[38;5;241m=\u001b[39mexpected_size,\n\u001b[1;32m 1016\u001b[0m filename\u001b[38;5;241m=\u001b[39mfilename,\n\u001b[1;32m 1017\u001b[0m force_download\u001b[38;5;241m=\u001b[39mforce_download,\n\u001b[1;32m 1018\u001b[0m )\n\u001b[1;32m 1019\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(pointer_path):\n\u001b[1;32m 1020\u001b[0m _create_symlink(blob_path, pointer_path, new_blob\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
78
+ "File \u001b[0;32m~/miniconda3/envs/py312/lib/python3.12/site-packages/huggingface_hub-0.27.1-py3.8.egg/huggingface_hub/file_download.py:1543\u001b[0m, in \u001b[0;36m_download_to_tmp_and_move\u001b[0;34m(incomplete_path, destination_path, url_to_download, proxies, headers, expected_size, filename, force_download)\u001b[0m\n\u001b[1;32m 1540\u001b[0m _check_disk_space(expected_size, incomplete_path\u001b[38;5;241m.\u001b[39mparent)\n\u001b[1;32m 1541\u001b[0m _check_disk_space(expected_size, destination_path\u001b[38;5;241m.\u001b[39mparent)\n\u001b[0;32m-> 1543\u001b[0m http_get(\n\u001b[1;32m 1544\u001b[0m url_to_download,\n\u001b[1;32m 1545\u001b[0m f,\n\u001b[1;32m 1546\u001b[0m proxies\u001b[38;5;241m=\u001b[39mproxies,\n\u001b[1;32m 1547\u001b[0m resume_size\u001b[38;5;241m=\u001b[39mresume_size,\n\u001b[1;32m 1548\u001b[0m headers\u001b[38;5;241m=\u001b[39mheaders,\n\u001b[1;32m 1549\u001b[0m expected_size\u001b[38;5;241m=\u001b[39mexpected_size,\n\u001b[1;32m 1550\u001b[0m )\n\u001b[1;32m 1552\u001b[0m logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDownload complete. Moving file to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdestination_path\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1553\u001b[0m _chmod_and_move(incomplete_path, destination_path)\n",
79
+ "File \u001b[0;32m~/miniconda3/envs/py312/lib/python3.12/site-packages/huggingface_hub-0.27.1-py3.8.egg/huggingface_hub/file_download.py:452\u001b[0m, in \u001b[0;36mhttp_get\u001b[0;34m(url, temp_file, proxies, resume_size, headers, expected_size, displayed_filename, _nb_retries, _tqdm_bar)\u001b[0m\n\u001b[1;32m 450\u001b[0m new_resume_size \u001b[38;5;241m=\u001b[39m resume_size\n\u001b[1;32m 451\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 452\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m r\u001b[38;5;241m.\u001b[39miter_content(chunk_size\u001b[38;5;241m=\u001b[39mconstants\u001b[38;5;241m.\u001b[39mDOWNLOAD_CHUNK_SIZE):\n\u001b[1;32m 453\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunk: \u001b[38;5;66;03m# filter out keep-alive new chunks\u001b[39;00m\n\u001b[1;32m 454\u001b[0m progress\u001b[38;5;241m.\u001b[39mupdate(\u001b[38;5;28mlen\u001b[39m(chunk))\n",
80
+ "File \u001b[0;32m~/miniconda3/envs/py312/lib/python3.12/site-packages/requests/models.py:820\u001b[0m, in \u001b[0;36mResponse.iter_content.<locals>.generate\u001b[0;34m()\u001b[0m\n\u001b[1;32m 818\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstream\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 819\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 820\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mraw\u001b[38;5;241m.\u001b[39mstream(chunk_size, decode_content\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 821\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ProtocolError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 822\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m ChunkedEncodingError(e)\n",
81
+ "File \u001b[0;32m~/miniconda3/envs/py312/lib/python3.12/site-packages/urllib3/response.py:1066\u001b[0m, in \u001b[0;36mHTTPResponse.stream\u001b[0;34m(self, amt, decode_content)\u001b[0m\n\u001b[1;32m 1064\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1065\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_fp_closed(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fp) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_decoded_buffer) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m-> 1066\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mread(amt\u001b[38;5;241m=\u001b[39mamt, decode_content\u001b[38;5;241m=\u001b[39mdecode_content)\n\u001b[1;32m 1068\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data:\n\u001b[1;32m 1069\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m data\n",
82
+ "File \u001b[0;32m~/miniconda3/envs/py312/lib/python3.12/site-packages/urllib3/response.py:955\u001b[0m, in \u001b[0;36mHTTPResponse.read\u001b[0;34m(self, amt, decode_content, cache_content)\u001b[0m\n\u001b[1;32m 952\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_decoded_buffer) \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m amt:\n\u001b[1;32m 953\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_decoded_buffer\u001b[38;5;241m.\u001b[39mget(amt)\n\u001b[0;32m--> 955\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_raw_read(amt)\n\u001b[1;32m 957\u001b[0m flush_decoder \u001b[38;5;241m=\u001b[39m amt \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m (amt \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m data)\n\u001b[1;32m 959\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m data \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_decoded_buffer) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
83
+ "File \u001b[0;32m~/miniconda3/envs/py312/lib/python3.12/site-packages/urllib3/response.py:879\u001b[0m, in \u001b[0;36mHTTPResponse._raw_read\u001b[0;34m(self, amt, read1)\u001b[0m\n\u001b[1;32m 876\u001b[0m fp_closed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fp, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mclosed\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 878\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_error_catcher():\n\u001b[0;32m--> 879\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fp_read(amt, read1\u001b[38;5;241m=\u001b[39mread1) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m fp_closed \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 880\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m amt \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m amt \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m data:\n\u001b[1;32m 881\u001b[0m \u001b[38;5;66;03m# Platform-specific: Buggy versions of Python.\u001b[39;00m\n\u001b[1;32m 882\u001b[0m \u001b[38;5;66;03m# Close the connection when no data is returned\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 887\u001b[0m \u001b[38;5;66;03m# not properly close the connection in all cases. There is\u001b[39;00m\n\u001b[1;32m 888\u001b[0m \u001b[38;5;66;03m# no harm in redundantly calling close.\u001b[39;00m\n\u001b[1;32m 889\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fp\u001b[38;5;241m.\u001b[39mclose()\n",
84
+ "File \u001b[0;32m~/miniconda3/envs/py312/lib/python3.12/site-packages/urllib3/response.py:862\u001b[0m, in \u001b[0;36mHTTPResponse._fp_read\u001b[0;34m(self, amt, read1)\u001b[0m\n\u001b[1;32m 859\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fp\u001b[38;5;241m.\u001b[39mread1(amt) \u001b[38;5;28;01mif\u001b[39;00m amt \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fp\u001b[38;5;241m.\u001b[39mread1()\n\u001b[1;32m 860\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 861\u001b[0m \u001b[38;5;66;03m# StringIO doesn't like amt=None\u001b[39;00m\n\u001b[0;32m--> 862\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fp\u001b[38;5;241m.\u001b[39mread(amt) \u001b[38;5;28;01mif\u001b[39;00m amt \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fp\u001b[38;5;241m.\u001b[39mread()\n",
85
+ "File \u001b[0;32m~/miniconda3/envs/py312/lib/python3.12/http/client.py:479\u001b[0m, in \u001b[0;36mHTTPResponse.read\u001b[0;34m(self, amt)\u001b[0m\n\u001b[1;32m 476\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlength \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m amt \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlength:\n\u001b[1;32m 477\u001b[0m \u001b[38;5;66;03m# clip the read to the \"end of response\"\u001b[39;00m\n\u001b[1;32m 478\u001b[0m amt \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlength\n\u001b[0;32m--> 479\u001b[0m s \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfp\u001b[38;5;241m.\u001b[39mread(amt)\n\u001b[1;32m 480\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m s \u001b[38;5;129;01mand\u001b[39;00m amt:\n\u001b[1;32m 481\u001b[0m \u001b[38;5;66;03m# Ideally, we would raise IncompleteRead if the content-length\u001b[39;00m\n\u001b[1;32m 482\u001b[0m \u001b[38;5;66;03m# wasn't satisfied, but it might break compatibility.\u001b[39;00m\n\u001b[1;32m 483\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_close_conn()\n",
86
+ "File \u001b[0;32m~/miniconda3/envs/py312/lib/python3.12/socket.py:707\u001b[0m, in \u001b[0;36mSocketIO.readinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m 705\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m 706\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 707\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sock\u001b[38;5;241m.\u001b[39mrecv_into(b)\n\u001b[1;32m 708\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m timeout:\n\u001b[1;32m 709\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_timeout_occurred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
87
+ "File \u001b[0;32m~/miniconda3/envs/py312/lib/python3.12/ssl.py:1252\u001b[0m, in \u001b[0;36mSSLSocket.recv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m 1248\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m flags \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 1249\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 1250\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnon-zero flags not allowed in calls to recv_into() on \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m\n\u001b[1;32m 1251\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m)\n\u001b[0;32m-> 1252\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mread(nbytes, buffer)\n\u001b[1;32m 1253\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1254\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mrecv_into(buffer, nbytes, flags)\n",
88
+ "File \u001b[0;32m~/miniconda3/envs/py312/lib/python3.12/ssl.py:1104\u001b[0m, in \u001b[0;36mSSLSocket.read\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m 1102\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m buffer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1104\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sslobj\u001b[38;5;241m.\u001b[39mread(\u001b[38;5;28mlen\u001b[39m, buffer)\n\u001b[1;32m 1105\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1106\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sslobj\u001b[38;5;241m.\u001b[39mread(\u001b[38;5;28mlen\u001b[39m)\n",
89
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
90
+ ]
91
+ }
92
+ ],
93
+ "source": [
94
+ "import torch\n",
95
+ "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
96
+ "from peft import PeftModel\n",
97
+ "import os\n",
98
+ "import gc\n",
99
+ "\n",
100
+ "# === 1. 环境准备 (MPS) ===\n",
101
+ "# 必须先 Restart Kernel，否则这里会报错\n",
102
+ "device = torch.device(\"mps\")\n",
103
+ "print(f\"🚀 环境已重置，使用设备: {device}\")\n",
104
+ "\n",
105
+ "# === 2. 加载模型 (MPS + Float16) ===\n",
106
+ "model_id = \"psychologyphd/CodeLlama-7b-Text-to-SQL-mps-finetuned\"\n",
107
+ "\n",
108
+ "\n",
109
+ "print(\"⏳ 正在加载模型 (MPS + Float16)...\")\n",
110
+ "\n",
111
+ "# 加载 Base Model\n",
112
+ "model = AutoModelForCausalLM.from_pretrained(\n",
113
+ " model_id,\n",
114
+ " device_map={\"\": device}, \n",
115
+ " torch_dtype=torch.float16, # 半精度，省显存\n",
116
+ ")\n",
117
+ "\n",
118
+ "\n",
119
+ "model.eval()\n",
120
+ "\n",
121
+ "# 加载 Tokenizer\n",
122
+ "tokenizer = AutoTokenizer.from_pretrained(base_model_id)\n",
123
+ "if tokenizer.pad_token is None:\n",
124
+ " tokenizer.pad_token = tokenizer.eos_token\n",
125
+ "\n",
126
+ "print(\"✅ 模型加载完毕\")\n",
127
+ "\n",
128
+ "# === 3. 构造【修复版】Prompt ===\n",
129
+ "# ⚠️ 关键点：这里严格保证了 SCHEMA 后的换行符\n",
130
+ "test_schema = \"CREATE TABLE table_name_66 (place VARCHAR, money___£__ VARCHAR, score VARCHAR)\"\n",
131
+ "test_question = \"What is the place of the player with £140,000 and a 68-68-75-68=279 score?\"\n",
132
+ "\n",
133
+ "prompt_manual = f\"### System: You are an text to SQL query translator. Users will ask you questions in English and you will generate a SQL query based on the provided SCHEMA.\\nSCHEMA:\\n{test_schema}\\n\"\n",
134
+ "prompt_manual += f\"### Human: {test_question}\\n\"\n",
135
+ "prompt_manual += \"### Assistant: \"\n",
136
+ "\n",
137
+ "print(f\"\\n🔍 Prompt (确认已有换行):\\n{prompt_manual}\\n{'='*20}\")\n",
138
+ "\n",
139
+ "# === 4. 生成 (带重复惩罚) ===\n",
140
+ "inputs = tokenizer(prompt_manual, return_tensors=\"pt\").to(device)\n",
141
+ "\n",
142
+ "print(\"🤖 正在生成 (MPS Fast Mode)...\")\n",
143
+ "with torch.no_grad():\n",
144
+ " outputs = model.generate(\n",
145
+ " **inputs,\n",
146
+ " max_new_tokens=100,\n",
147
+ " do_sample=True, # 开启采样以支持 repetition_penalty\n",
148
+ " temperature=0.1, # 低温，保持生成的 SQL 准确\n",
149
+ " top_p=0.95,\n",
150
+ " repetition_penalty=1.2, # 🔴 强力压制复读机\n",
151
+ " pad_token_id=tokenizer.pad_token_id,\n",
152
+ " eos_token_id=tokenizer.eos_token_id\n",
153
+ " )\n",
154
+ "\n",
155
+ "# === 5. 解码 ===\n",
156
+ "result = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
157
+ "if \"### Assistant:\" in result:\n",
158
+ " final_sql = result.split(\"### Assistant:\")[-1].strip()\n",
159
+ "else:\n",
160
+ " final_sql = result[len(prompt_manual):].strip()\n",
161
+ "\n",
162
+ "print(f\"\\n💡 生成结果:\\n{final_sql}\")"
163
+ ]
164
+ },
165
+ {
166
+ "cell_type": "markdown",
167
+ "id": "7cd8644d",
168
+ "metadata": {},
169
+ "source": [
170
+ "it can gernerate results like the following. it is able to under stand from the begining that the task is to generate sql. but it is overfitting that resulted in repeated users and \\\\\\\\\\. \n",
171
+ "💡 生成结果:\n",
172
+ "SELECT place tagUsersusers Users \\\\3\\\\ Users Users\\\\­\\\\\\\\\\\\\\\\\\\\ Users Users Users\\\\\\\\\\\\\\\\ //\\\\\\\\ Users\\\\ Users Users\\\\\\\\\\\\\\\\ # \\\\\\\\\\\\\\\\\\\\\\\\\\\\ \\\\\\\\ <? Users\\\\\\\\\\\\\\\\\\\\\\\\ \\\\ \\\\\\\\\\\\\\\\ Users\\\\ Users package\\\\ Users\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\ <\\\\\\\\ import\\\\\\\\\\\\\\\\ \\\\ --- '\\\\\\\\\\\\\\\\ \\\\"
173
+ ]
174
+ },
175
+ {
176
+ "cell_type": "markdown",
177
+ "id": "a3a8f0e3",
178
+ "metadata": {},
179
+ "source": []
180
+ },
181
+ {
182
+ "cell_type": "markdown",
183
+ "id": "822babe2",
184
+ "metadata": {},
185
+ "source": []
186
+ }
187
+ ],
188
+ "metadata": {
189
+ "kernelspec": {
190
+ "display_name": "Python 3.12.2",
191
+ "language": "python",
192
+ "name": "py312"
193
+ },
194
+ "language_info": {
195
+ "codemirror_mode": {
196
+ "name": "ipython",
197
+ "version": 3
198
+ },
199
+ "file_extension": ".py",
200
+ "mimetype": "text/x-python",
201
+ "name": "python",
202
+ "nbconvert_exporter": "python",
203
+ "pygments_lexer": "ipython3",
204
+ "version": "3.12.2"
205
+ }
206
+ },
207
+ "nbformat": 4,
208
+ "nbformat_minor": 5
209
+ }