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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Chatbot Development\n",
"\n",
"Use this notebook to load the model and then initialize, update, and test the chatbot."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Setup and Imports"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from huggingface_hub import login\n",
"\n",
"\n",
"from src.chat import Chatbot\n",
"from config import BASE_MODEL, MY_MODEL"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\"\"\"\n",
"TODO: Add your Hugging Face token\n",
"Options:\n",
"1. Use login() and enter token when prompted. It won't ask for your token if you already logged in using the command: huggingface-cli login in the terminal.\n",
"2. Set environment variable HUGGINGFACE_TOKEN\n",
"3. Pass token directly (not recommended for shared notebooks)\n",
"\"\"\"\n",
"\n",
"login()\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Initialize and test chatbot"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"\"\"\"\n",
"Create chatbot instance using chat.py\n",
"\"\"\"\n",
"chatbot = Chatbot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\"\"\"\n",
"Test out generating some responses from the chatbot.\n",
"Inference time\n",
"\"\"\"\n",
"test_question = \"What options are available for someone in my situation?\"\n",
"\n",
"print(f\"\\nQuestion: {test_question}\")\n",
"response = chatbot.get_response(test_question)\n",
"print(f\"Response: {response}\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# TODO: Update pre-trained Llama to be a task-specific chatbot\n",
"\n",
"This part is up to you! You might want to finetune the model, simply make a really good system prompt, use RAG, provide the model relevant data in-context, etc. Be creative!\n",
"\n",
"You can also feel free to do this in another script and then evaluate the model here.\n",
"\n",
"Tips:\n",
"- HuggingFace has built-in methods to finetune models, if you choose that route. Take advantage of those methods! You can then save your new, finetuned model in the HuggingFace Hub. Change MY_MODEL in config.py to the name of the model in the hub to make your chatbot use it.\n",
"- You may also want to consider LoRA if you choose finetuning."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "venv",
"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",
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