Upload Falcon.ipynb

Falcon.ipynb

@@ -0,0 +1,251 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " # Install Dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117 --upgrade"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install langchain einops accelerate transformers bitsandbytes"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Import Dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "d:\\YouTube\\6-06-2023 - Falcon\\falcon\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain import HuggingFacePipeline\n",
+    "from langchain import PromptTemplate,  LLMChain\n",
+    "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
+    "import transformers\n",
+    "import os \n",
+    "import torch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check if cuda is available \n",
+    "torch.cuda.is_available()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Build the Pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define Model ID\n",
+    "model_id = \"tiiuae/falcon-40b-instruct\"\n",
+    "# Load Tokenizer\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
+    "# Load Model \n",
+    "model = AutoModelForCausalLM.from_pretrained(model_id, cache_dir='./workspace/', \n",
+    "    torch_dtype=torch.bfloat16, trust_remote_code=True, device_map=\"auto\", offload_folder=\"offload\")\n",
+    "# Set PT model to inference mode\n",
+    "model.eval()\n",
+    "# Build HF Transformers pipeline \n",
+    "pipeline = transformers.pipeline(\n",
+    "    \"text-generation\", \n",
+    "    model=model,\n",
+    "    tokenizer=tokenizer,\n",
+    "    device_map=\"auto\",\n",
+    "    max_length=400,\n",
+    "    do_sample=True,\n",
+    "    top_k=10,\n",
+    "    num_return_sequences=1,\n",
+    "    eos_token_id=tokenizer.eos_token_id\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Test out the pipeline\n",
+    "pipeline('who is kim kardashian?')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pass it to Langchain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Setup prompt template\n",
+    "template = PromptTemplate(input_variables=['input'], template='{input}') \n",
+    "# Pass hugging face pipeline to langchain class\n",
+    "llm = HuggingFacePipeline(pipeline=pipeline) \n",
+    "# Build stacked LLM chain i.e. prompt-formatting + LLM\n",
+    "chain = LLMChain(llm=llm, prompt=template)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Test LLMChain \n",
+    "response = chain.run('who is kim kardashian?')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Build Gradio App"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install Gradio for the UI component\n",
+    "!pip install gradio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import gradio for UI\n",
+    "import gradio as gr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create generate function - this will be called when a user runs the gradio app \n",
+    "def generate(prompt): \n",
+    "    # The prompt will get passed to the LLM Chain!\n",
+    "    return chain.run(prompt)\n",
+    "    # And will return responses "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define a string variable to hold the title of the app\n",
+    "title = '🦜🔗 Falcon-40b-Instruct'\n",
+    "# Define another string variable to hold the description of the app\n",
+    "description = 'This application demonstrates the use of the open-source `Falcon-40b-Instruct` LLM.'\n",
+    "# pls subscribe 🙏"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Build gradio interface, define inputs and outputs...just text in this\n",
+    "gr.Interface(fn=generate, inputs=[\"text\"], outputs=[\"text\"], \n",
+    "             # Pass through title and description\n",
+    "             title=title, description=description, \n",
+    "             # Set theme and launch parameters\n",
+    "             theme='finlaymacklon/boxy_violet').launch(server_port=8080, share=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "falcon",
+   "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": "3.9.12"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}