From 34661108aa3441b0dcd0efa1c4e4c79b147e4f2d Mon Sep 17 00:00:00 2001 From: Mike Date: Wed, 23 Jul 2025 12:46:58 +0100 Subject: [PATCH] Added AI-powered synthetic dataset generator demo --- .../synthetic-dataset-generator/README.md | 37 + .../synthgen.ipynb | 4814 +++++++++++++++++ 2 files changed, 4851 insertions(+) create mode 100644 community-contributions/synthetic-dataset-generator/README.md create mode 100644 community-contributions/synthetic-dataset-generator/synthgen.ipynb diff --git a/community-contributions/synthetic-dataset-generator/README.md b/community-contributions/synthetic-dataset-generator/README.md new file mode 100644 index 0000000..4148810 --- /dev/null +++ b/community-contributions/synthetic-dataset-generator/README.md @@ -0,0 +1,37 @@ +# LLM-Powered Dataset Synthesizer: LLaMA 3 + Gradio Demo + +This interactive demo showcases a synthetic dataset generation pipeline powered by Meta's LLaMA 3.1 8B-Instruct model, running in 4-bit quantized mode. Users can input natural language prompts describing the structure and logic of a desired dataset, and the model will generate tabular data accordingly. + +## ✨ Description + +Modern LLMs are capable of reasoning over structured data formats and generating realistic, constrained datasets. This demo leverages the LLaMA 3.1 instruct model, combined with prompt engineering, to generate high-quality synthetic tabular data from plain-language descriptions. + +Key components: +- **LLaMA 3.1 8B-Instruct** via Hugging Face Transformers +- **4-bit quantized loading** with `bitsandbytes` for memory efficiency +- **Custom prompt framework** for schema + value constraints +- **Interactive interface** built with Gradio for user-friendly data generation + +## πŸš€ Functionality + +With this tool, you can: +- Generate synthetic datasets by describing the column names, data types, value logic, and number of rows +- Apply constraints based on age, gender, matching conditions, and more (e.g., β€œfemales over 40; males under 40”) +- Preview the raw model output or extract structured JSON/tabular results +- Interactively explore and copy generated datasets from the Gradio UI + +## πŸ› οΈ Under the Hood + +- The model prompt template includes both a **system message** and user instruction +- Output is parsed to extract valid JSON objects +- The generated data is displayed in the Gradio interface and downloadable as CSV + +## πŸ“¦ Requirements + +- Python (Colab recommended) +- `transformers`, `bitsandbytes`, `accelerate`, `gradio`, `torch` +- Hugging Face access token with permission to load LLaMA 3.1 + +--- + +Ready to generate smart synthetic datasets with just a sentence? Try it! diff --git a/community-contributions/synthetic-dataset-generator/synthgen.ipynb b/community-contributions/synthetic-dataset-generator/synthgen.ipynb new file mode 100644 index 0000000..872e368 --- /dev/null +++ b/community-contributions/synthetic-dataset-generator/synthgen.ipynb @@ -0,0 +1,4814 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "953b642e", + "metadata": {}, + "source": [ + "# SynthGen: an LLM-Powered Dataset Synthesizer\n", + "> ⚑ Powered by LLaMa 3 from HuggingFace and an intuitive Gradio UI.\n", + "\n", + "This notebook demonstrates how to generate realistic, structured tabular data using natural language prompts powered by the Meta-LLaMA-3.1 8B-Instruct model. By specifying column names, value types, and logical constraints in plain English, users can produce tailored synthetic datasets interactively via a Gradio interface.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "a8a50195", + "metadata": {}, + "source": [ + "### 1. Installing Required Packages" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0d7817f1", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0d7817f1", + "outputId": "8ae1c98c-c3da-44de-a908-441cf45c5ee8" + }, + "outputs": [], + "source": [ + "!pip install -q --upgrade torch==2.5.1+cu124 torchvision==0.20.1+cu124 torchaudio==2.5.1+cu124 --index-url https://download.pytorch.org/whl/cu124\n", + "!pip install -q requests bitsandbytes==0.46.0 transformers==4.48.3 accelerate==1.3.0" + ] + }, + { + "cell_type": "markdown", + "id": "b11fc9c1", + "metadata": {}, + "source": [ + "## 2. Importing Libraries and HF Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c1c323f2", + "metadata": { + "id": "c1c323f2" + }, + "outputs": [], + "source": [ + "import os\n", + "from IPython.display import Markdown, display, update_display\n", + "\n", + "import gradio as gr\n", + "import pandas as pd\n", + "\n", + "from google.colab import userdata\n", + "from huggingface_hub import login\n", + "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n", + "import torch\n", + "import gc\n", + "import json\n", + "import re\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cbfbb506", + "metadata": { + "id": "cbfbb506" + }, + "outputs": [], + "source": [ + "hf_token = userdata.get('HF_TOKEN')\n", + "login(hf_token, add_to_git_credential=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "koICwnTwQMVP", + "metadata": { + "id": "koICwnTwQMVP" + }, + "outputs": [], + "source": [ + "# We are using the Meta-Llama-3.1-8B-Instruct model. Feel free to change this to any other model you prefer, \n", + "# by adjusting the model variant accordingly, and ensuring the template is compatible.\n", + "model_variant = \"meta-llama/Meta-Llama-3.1-8B-Instruct\"" + ] + }, + { + "cell_type": "markdown", + "id": "a817a180", + "metadata": {}, + "source": [ + "### 3. Loading the LLaMA 3.1 Model (Quantized)\n", + "Depending on the model, this may take a few." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "JbZP17wkSdW0", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 456, + "referenced_widgets": [ + "9dde77220ce04b80ac2b999d9304ac61", + "3562239e91694d618538fabbf55bdc7e", + "90d6add2cac446958326cb3cb4defc35", + "36897c09ed284e9ebad8b610186c5c2f", + "19f90c9b7f6b4fb4b7e4db0fda3697e6", + "037a38e5d42b4643a40e125d1ca7aa9d", + "114ae2bee64b4ca09b0f06e8d7f154c7", + "4e0a5f3cf25b4d4bb4248f3a686a3b2b", + "3d71197f73b540cc8a0702165b336ee6", + "4ace0ac8c70b43f9b524e82b7102b492", + "6ee18cbecc5c446782764a7a7a2093ed", + "ea8987a8a81a4c1ca33ba1f6d35eabf7", + "9181116e0dab421e8538c3b5e6d78a63", + "06fc98bc230248618f718cc789860551", + "b7f4683f24f6497084de01fc9dcb7b4c", + "216a21cb431c4efcb25118d60ce55382", + "3d3301ac71ed4e988deeafb5b29b1e7e", + "4ee7c6a29caa45589987d7da4fcebca2", + 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"aa52d70d64b7456eb99cc9386a6b773b", + "31a933bedaad4d5489a4983bd6ca9e6f", + "6e115f191b664f59b8144719ac6d6d13", + "03fd43b58e74455992d71a1625d368ad", + "bd0133b89f784f16ab20ff28574d537b", + "2ad775a43a284f489a7f7fef1c20dbb9", + "b1a13112ead34742835e635910820dd0", + "4697e1265559409f82f463f339a5f62c", + "31f68d4b6f624040899436006275613d", + "8e93a17e01b64ff89f3220565ada3082", + "79dbb408b3cf498895c8b066dd04b1c5", + "924bb29fc7134a0b98187770d0943d3d", + "161f9b5e075b404c871a58fbd560d63d", + "2dbcf9f4ebb54761802ef3bda76f2294", + "c1d3120bad3d456285a0807445d5e4ee", + "df49ac181ec244f5bebe6680408fd7ee", + "7836c3b013ec4f67a2b09670878581ba", + "f3467a09b50a4b429ff75009a26d992b", + "b9d1f51b79d34264965e512bfc0253b3", + "06722a988e1a4357a3841723aa66dfa8", + "46f37131f36a413badffb0c362ab8b5d", + "0e1f48ecaf6e4184953ec773bed24253", + "38ee56d491c74186a2146cac776a631b", + "e59cfa5ca5b240159517ccc2a594fa50", + "e18941e122eb4332b9e5fb1c3a22b9ed", + "2711dbd8740b453c849026f3d83f47af", + "0d8f09aba9514921baba774dcc1dd088", + "4a10cac721514d30b6c10978d33c6e72", + "f9d1ba01c0054ae3917a78e420174f2e", + "e95f552f62344e52aa2494fbaa0e1f16", + "85c5b698708b47319067527bda04445f", + "2bec34b01cfb452787013ab7bcef1be3", + "2c7826be2c25431081eb05ee2d7d4401", + "5c2b5412ceb54502b5018f2b756aa291", + "001b683aec074eb98b498f0dec667b7c", + "eb0e9fb2bf8447748a936749dceff1b9" + ] + }, + "id": "JbZP17wkSdW0", + "outputId": "b19f6cc6-872d-42ce-8c08-f750052ea7e6" + }, + "outputs": [], + "source": [ + "quant_config = BitsAndBytesConfig(\n", + "load_in_4bit=True,\n", + "bnb_4bit_use_double_quant=True,\n", + "bnb_4bit_compute_dtype=torch.bfloat16,\n", + "bnb_4bit_quant_type=\"nf4\"\n", + ")\n", + "\n", + "model = AutoModelForCausalLM.from_pretrained(\n", + " model_variant,\n", + " quantization_config=quant_config,\n", + " device_map=\"auto\",\n", + " trust_remote_code=True,\n", + " use_auth_token=True\n", + ")\n", + "\n", + "\n", + "tokenizer = AutoTokenizer.from_pretrained(model_variant)\n", + "tokenizer.pad_token = tokenizer.eos_token" + ] + }, + { + "cell_type": "markdown", + "id": "1877a8cb", + "metadata": {}, + "source": [ + "### 4. Prompt Construction and Inference\n", + "\n", + "In this section, we've crafted a flexible prompt template designed to guide the LLM toward generating clean, structured output that Gradio can easily process. The goal is to balance natural language flexibility with enough constraints to encourage valid, consistent, and readable tabular data.\n", + "\n", + "That said, LLMs can still be unpredictable or inconsistent, especially with loosely defined instructions. Feel free to adjust the prompt logic or system message to better suit your use case or to improve output reliability.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "935477d2", + "metadata": { + "id": "935477d2" + }, + "outputs": [], + "source": [ + "\n", + "def build_system_query(user_instructions: str = '', include_system_prompt: bool = True):\n", + " messages = [\n", + " {\n", + " \"role\": \"system\",\n", + " \"content\": (\n", + " \"You are a dataset generation assistant. The user will provide prompts containing column names, value types, number of rows, and other parameters and constraints.\\n\"\n", + " \"Your task is to generate synthetic datasets based on the user's request.\\n\\n\"\n", + " \"Always respond with a valid JSON object (dictionary), where:\\n\"\n", + " \"- Each key is a column name (formatted for pandas).\\n\"\n", + " \"- Each value is a list of values for that column.\\n\\n\"\n", + " \"Example format:\\n\"\n", + " \"{\\n\"\n", + " \" \\\"first_name\\\": [\\\"Alex\\\", \\\"Anthony\\\", \\\"Ava\\\", \\\"Amber\\\", \\\"Annabelle\\\"],\\n\"\n", + " \" \\\"age\\\": [23, 27, 35, 29, 31]\\n\"\n", + " \"}\\n\\n\"\n", + " \"If a value type or constraint is missing or unclear, make a reasonable assumption based on the column name and context.\\n\"\n", + " \"Do not include any explanations, comments, or extra text β€” only the raw JSON.\\n\"\n", + " \"Ensure the response is compact, well-formatted, and syntactically valid for parsing by JSON tools or conversion into a pandas DataFrame.\\n\"\n", + " \"Convert column names to pandas-compatible formats when needed (e.g., replace spaces with underscores, remove special characters, lowercase).\\n\"\n", + " )\n", + "\n", + " },\n", + " {\"role\": \"user\", \"content\": user_instructions}\n", + " ]\n", + "\n", + " return messages if include_system_prompt else messages[1:]" + ] + }, + { + "cell_type": "markdown", + "id": "83c6b3c2", + "metadata": {}, + "source": [ + "### 4.5 Let's Test It! \n", + "*(The following query intentionally includes some typos)*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "tr29FmHQTkzy", + "metadata": { + "id": "tr29FmHQTkzy" + }, + "outputs": [], + "source": [ + "user_query = \"Generate 3 columns, firstNAme, last nam, aGe, 10 rows. Male names should correspond to an age range between 30 and 50.\"\n", + "user_query += \"Female names should be between 34 and 40. Make sure they match row-wise. \\n\"\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "SWAGfc39RxWr", + "metadata": { + "id": "SWAGfc39RxWr" + }, + "outputs": [], + "source": [ + "messages = build_system_query(user_instructions=user_query, include_system_prompt=True)\n", + "inputs = tokenizer.apply_chat_template(messages, return_tensors=\"pt\").to(\"cuda\")\n", + "\n", + "outputs = model.generate(\n", + " inputs,\n", + " max_new_tokens=800,\n", + " do_sample=True,\n", + " temperature=0.7,\n", + " top_p=0.95,\n", + " pad_token_id=tokenizer.eos_token_id,\n", + " eos_token_id=tokenizer.eos_token_id # Optional, to reinforce stopping\n", + ")\n", + "decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "JgAGOumVHw8W", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JgAGOumVHw8W", + "outputId": "b4853467-d868-4636-a80e-0c39260c4d93" + }, + "outputs": [], + "source": [ + "# Let's first print the full output to see what we got\n", + "print(decoded)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "gpu0Zji2DtrV", + "metadata": { + "id": "gpu0Zji2DtrV" + }, + "outputs": [], + "source": [ + "# The following function attempts to extract a valid JSON response from the model's output.\n", + "# It searches for the last valid JSON object in the text, which is useful if the model\n", + "# generates additional text or explanations that are not part of the JSON response.\n", + "def extract_response(text: str):\n", + " decoder = json.JSONDecoder()\n", + " starts = [m.start() for m in re.finditer(r'{', text)]\n", + " for pos in reversed(starts): # Start from last candidate\n", + " try:\n", + " parsed, _ = decoder.raw_decode(text[pos:])\n", + " if isinstance(parsed, dict):\n", + " return parsed\n", + " except json.JSONDecodeError:\n", + " continue\n", + " return None\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "MwofOSXTqWA-", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "id": "MwofOSXTqWA-", + "outputId": "9bc87b6e-4aca-44b6-956b-49ef4830d1d1" + }, + "outputs": [], + "source": [ + "# Convert the extracted JSON response into a pandas DataFrame\n", + "if decoded:\n", + " pd.DataFrame(extract_response(decoded))" + ] + }, + { + "cell_type": "markdown", + "id": "3e4e5fbe", + "metadata": {}, + "source": [ + "### 5. Interactive UI with Gradio" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9448bc9a", + "metadata": { + "id": "9448bc9a" + }, + "outputs": [], + "source": [ + "# The following functions are used to handle the Gradio interface and dataset management.\n", + "\n", + "\n", + "def remove_items(selected_features, existing_dataframe: pd.DataFrame):\n", + " \"\"\"Remove selected features from the existing DataFrame.\"\"\"\n", + " print(\"Before removal:\", existing_dataframe)\n", + "\n", + " if isinstance(selected_features, str):\n", + " selected_features = [selected_features]\n", + "\n", + " edited_df = existing_dataframe.copy()\n", + "\n", + " if selected_features:\n", + " edited_df.drop(columns=selected_features, axis=1, inplace=True)\n", + "\n", + " # Force clean index/column structure\n", + " edited_df = edited_df.copy() # Ensures no hidden pandas artifacts\n", + "\n", + " updated_features_list = list(edited_df.columns)\n", + "\n", + " print(\"After removal:\", edited_df)\n", + " print('shape', edited_df.shape[0])\n", + " return (\n", + " gr.update(choices=updated_features_list, value=[]),\n", + " edited_df,\n", + " gr.update(value=edited_df, visible=not edited_df.empty),\n", + " gr.update(interactive=edited_df.empty)\n", + " )\n", + "\n", + "\n", + "def generate_features(n_rows: str, instructions: str, existing_dataframe: pd.DataFrame):\n", + " \"\"\"Generate new features based on user instructions and existing data.\"\"\"\n", + " has_valid_rows = n_rows.isnumeric() and int(n_rows) >= 1\n", + " if has_valid_rows and instructions:\n", + " try:\n", + " # Prepare prompt for the model\n", + " user_query = instructions.strip() + f\"\\nGenerate {n_rows} rows.\"\n", + " messages = build_system_query(user_instructions=user_query, include_system_prompt=True)\n", + "\n", + " # Tokenize and generate\n", + " inputs = tokenizer.apply_chat_template(messages, return_tensors=\"pt\").to(\"cuda\")\n", + " outputs = model.generate(\n", + " inputs,\n", + " max_new_tokens=800,\n", + " do_sample=True,\n", + " temperature=0.7,\n", + " top_p=0.95,\n", + " pad_token_id=tokenizer.eos_token_id,\n", + " eos_token_id=tokenizer.eos_token_id,\n", + " )\n", + "\n", + " # Decode and parse model output\n", + " decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\n", + " new_df = pd.DataFrame(extract_response(decoded.split(\"assistant\")[-1]))\n", + "\n", + " # Combine with existing data\n", + " if len(existing_dataframe) == 0:\n", + " updated_df = new_df\n", + " elif len(existing_dataframe) == len(new_df):\n", + " updated_df = pd.concat([existing_dataframe.reset_index(drop=True),\n", + " new_df.reset_index(drop=True)], axis=1)\n", + " else:\n", + " raise ValueError(\"Row count mismatch between existing and new feature data.\")\n", + "\n", + " # UI component updates\n", + " rows_component_update = gr.update(interactive=False)\n", + " table_view_update = gr.update(visible=True, value=updated_df, headers=list(updated_df.columns))\n", + " feature_display_update = gr.update(choices=list(updated_df.columns))\n", + "\n", + " return rows_component_update, table_view_update, feature_display_update, updated_df, ''\n", + "\n", + " except Exception as e:\n", + " print(\"Error generating feature:\", e)\n", + "\n", + " # Return empty updates if input is invalid\n", + " return (gr.update(),) * 5\n", + "\n", + "def on_selected_feture(selected_items):\n", + " \"\"\"Update the UI based on selected features.\"\"\"\n", + " return gr.update(interactive=not selected_items==[])\n", + "\n", + "def export_dataset(dataframe: pd.DataFrame):\n", + " \"\"\"Export the DataFrame to a CSV file.\"\"\"\n", + " try:\n", + " n = 0\n", + " while True:\n", + " if n == 0:\n", + " filename = \"dataset.csv\"\n", + " else:\n", + " filename = f\"dataset{n}.csv\"\n", + " if not os.path.exists(filename):\n", + " break\n", + " n += 1\n", + "\n", + " dataframe.to_csv(filename, index=False)\n", + " except Exception as e:\n", + " print(\"Error exporting dataset:\", e)\n", + "\n", + "\n", + "\n", + "def on_table_change(changed_data):\n", + " \"\"\"Handle changes in the table and return a DataFrame.\"\"\"\n", + " print('on table change')\n", + " df = pd.DataFrame(changed_data)\n", + " return df if not df.empty else pd.DataFrame(), df.shape[0] if df.shape[0] > 0 else None\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8d7855f1", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 682 + }, + "id": "8d7855f1", + "outputId": "a6517655-4861-431d-fa13-fa1f93bce503" + }, + "outputs": [], + "source": [ + "\n", + "# UI layout\n", + "with gr.Blocks() as demo:\n", + " feature_state = gr.State(pd.DataFrame())\n", + "\n", + " with gr.Row():\n", + " # Left Column: Inputs and Controls\n", + " with gr.Column(scale=0):\n", + " with gr.Group():\n", + " gr.Markdown(\"### 🧾 Current Feature List\")\n", + " feature_display = gr.CheckboxGroup(\n", + " #choices=feature_dictionary,\n", + " label=\"Features\",\n", + " info=\"Select features to remove\"\n", + " )\n", + " with gr.Row(elem_classes=\"centered-button-row\"):\n", + " remove_btn = gr.Button(value=\"Remove Selected\", elem_classes=\"small-button\", interactive=False)\n", + "\n", + " # Add feature section\n", + " with gr.Group():\n", + " gr.Markdown(\"### βž• Add a New Feature\")\n", + " with gr.Row(equal_height=True):\n", + " n_rows = gr.Text(\n", + " label=\"Number of Rows\",\n", + " placeholder=\"e.g., 100\"\n", + " )\n", + "\n", + " instructions = gr.Text(\n", + " label=\"Instructions\",\n", + " placeholder=\"e.g., first names; starting with A or B. \\nAge, numeric; range 21–55, males should be between 30-40. Correlate rows\",\n", + " scale=1,\n", + " lines=4\n", + " )\n", + "\n", + " with gr.Row(elem_classes=\"centered-button-row\"):\n", + " add_btn = gr.Button(value=\"Add\", elem_classes=\"small-button\")\n", + "\n", + " # Dataset generation section\n", + " with gr.Group():\n", + " with gr.Row(elem_classes=\"centered-button-row\"):\n", + " export_btn = gr.Button(value=\"πŸ’Ύ Export...\", elem_classes=\"small-button\")\n", + "\n", + " # Right Column: Dataset display\n", + " with gr.Column():\n", + " gr.Markdown(\"### πŸ“Š Generated Dataset\")\n", + " table_view = gr.Dataframe(\n", + " interactive=True,\n", + " visible=False,\n", + " label=\"Dataset\"\n", + " )\n", + "\n", + " feature_display.change(\n", + " fn=on_selected_feture,\n", + " inputs=[feature_display],\n", + " outputs=[remove_btn]\n", + " )\n", + "\n", + " export_btn.click(\n", + " fn=export_dataset,\n", + " inputs=[feature_state]\n", + " )\n", + "\n", + "\n", + " remove_btn.click(\n", + " fn=remove_items,\n", + " inputs=[feature_display, feature_state],\n", + " outputs=[feature_display, feature_state, table_view, n_rows]\n", + " )\n", + "\n", + " add_btn.click(\n", + " fn=generate_features,\n", + " inputs=[n_rows, instructions, feature_state],\n", + " outputs=[n_rows, table_view, feature_display, feature_state, instructions]\n", + " )\n", + "\n", + " table_view.change(\n", + " fn=on_table_change,\n", + " inputs=[table_view],\n", + " outputs=[feature_state, n_rows]\n", + " )\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "c5350c20", + "metadata": {}, + "source": [ + "### Let's test it!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1c3bc056", + "metadata": { + "id": "1c3bc056" + }, + "outputs": [], + "source": [ + "demo.launch(debug=True) # Set debug=True to see detailed error messages in the console\n", + "# demo.launch(share=True) # Uncomment this line to share the app publicly" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + 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