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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "kU2JrcPlhwd9"
},
"outputs": [],
"source": [
"!pip install -q requests torch bitsandbytes transformers sentencepiece accelerate gradio"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lAMIVT4iwNg0"
},
"source": [
"**Imports**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "-Apd7-p-hyLk"
},
"outputs": [],
"source": [
"import os\n",
"import requests\n",
"from google.colab import drive\n",
"from huggingface_hub import login\n",
"from google.colab import userdata\n",
"from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer, BitsAndBytesConfig\n",
"import torch\n",
"import gradio as gr\n",
"\n",
"hf_token = userdata.get('HF_TOKEN')\n",
"login(hf_token, add_to_git_credential=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xa0qYqZrwQ66"
},
"source": [
"**Model**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "z5enGmuKjtJu"
},
"outputs": [],
"source": [
"model_name = \"meta-llama/Meta-Llama-3.1-8B-Instruct\"\n",
"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_name,\n",
" device_map=\"auto\",\n",
" quantization_config=quant_config\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "y1hUSmWlwSbp"
},
"source": [
"**Tokenizer**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "WjxNWW6bvdgj"
},
"outputs": [],
"source": [
"tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
"tokenizer.pad_token = tokenizer.eos_token"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1pg2U-B3wbIK"
},
"source": [
"**Functions**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ZvljDKdji8iV"
},
"outputs": [],
"source": [
"def generate_dataset(topic, number_of_data, inst1, resp1, inst2, resp2, inst3, resp3):\n",
" # Convert user inputs into multi-shot examples\n",
" multi_shot_examples = [\n",
" {\"instruction\": inst1, \"response\": resp1},\n",
" {\"instruction\": inst2, \"response\": resp2},\n",
" {\"instruction\": inst3, \"response\": resp3}\n",
" ]\n",
"\n",
" # System prompt\n",
" system_prompt = f\"\"\"\n",
" You are a helpful assistant whose main purpose is to generate datasets.\n",
" Topic: {topic}\n",
" Return the dataset in JSON format. Use examples with simple, fun, and easy-to-understand instructions for kids.\n",
" Include the following examples: {multi_shot_examples}\n",
" Return {number_of_data} examples each time.\n",
" Do not repeat the provided examples.\n",
" \"\"\"\n",
"\n",
" # Example Messages\n",
" messages = [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": f\"Please generate my dataset for {topic}\"}\n",
" ]\n",
"\n",
" # Tokenize Input\n",
" inputs = tokenizer.apply_chat_template(messages, return_tensors=\"pt\").to(\"cuda\")\n",
" streamer = TextStreamer(tokenizer)\n",
"\n",
" # Generate Output\n",
" outputs = model.generate(inputs, max_new_tokens=2000, streamer=streamer)\n",
"\n",
" # Decode and Return\n",
" return tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
"\n",
"\n",
"def gradio_interface(topic, number_of_data, inst1, resp1, inst2, resp2, inst3, resp3):\n",
" return generate_dataset(topic, number_of_data, inst1, resp1, inst2, resp2, inst3, resp3)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_WDZ5dvRwmng"
},
"source": [
"**Default Values**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "JAdfqYXnvEDE"
},
"outputs": [],
"source": [
"default_topic = \"Talking to a (5-8) years old and teaching them manners.\"\n",
"default_number_of_data = 10\n",
"default_multi_shot_examples = [\n",
" {\n",
" \"instruction\": \"Why do I have to say please when I want something?\",\n",
" \"response\": \"Because it’s like magic! It shows you’re nice, and people want to help you more.\"\n",
" },\n",
" {\n",
" \"instruction\": \"What should I say if someone gives me a toy?\",\n",
" \"response\": \"You say, 'Thank you!' because it makes them happy you liked it.\"\n",
" },\n",
" {\n",
" \"instruction\": \"why should I listen to my parents?\",\n",
" \"response\": \"Because parents want the best for you and they love you the most.\"\n",
" }\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JwZtD032wuK8"
},
"source": [
"**Init gradio**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "xy2RP5T-vxXg"
},
"outputs": [],
"source": [
"gr_interface = gr.Interface(\n",
" fn=gradio_interface,\n",
" inputs=[\n",
" gr.Textbox(label=\"Topic\", value=default_topic, lines=2),\n",
" gr.Number(label=\"Number of Examples\", value=default_number_of_data, precision=0),\n",
" gr.Textbox(label=\"Instruction 1\", value=default_multi_shot_examples[0][\"instruction\"]),\n",
" gr.Textbox(label=\"Response 1\", value=default_multi_shot_examples[0][\"response\"]),\n",
" gr.Textbox(label=\"Instruction 2\", value=default_multi_shot_examples[1][\"instruction\"]),\n",
" gr.Textbox(label=\"Response 2\", value=default_multi_shot_examples[1][\"response\"]),\n",
" gr.Textbox(label=\"Instruction 3\", value=default_multi_shot_examples[2][\"instruction\"]),\n",
" gr.Textbox(label=\"Response 3\", value=default_multi_shot_examples[2][\"response\"]),\n",
" ],\n",
" outputs=gr.Textbox(label=\"Generated Dataset\")\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HZx-mm9Uw3Ph"
},
"source": [
"**Run the app**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "bfGs5ip8mndg"
},
"outputs": [],
"source": [
"gr_interface.launch()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Cveqx392x7Mm"
},
"outputs": [],
"source": []
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"gpuType": "T4",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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