{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# C Transformers\n",
    "\n",
    "The [C Transformers](https://github.com/marella/ctransformers) library provides Python bindings for GGML models.\n",
    "\n",
    "This example goes over how to use LangChain to interact with `C Transformers` [models](https://github.com/marella/ctransformers#supported-models)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Install**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install ctransformers"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Load Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.llms import CTransformers\n",
    "\n",
    "llm = CTransformers(model='marella/gpt-2-ggml')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Generate Text**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(llm('AI is going to'))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Streaming**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
    "\n",
    "llm = CTransformers(model='marella/gpt-2-ggml', callbacks=[StreamingStdOutCallbackHandler()])\n",
    "\n",
    "response = llm('AI is going to')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**LLMChain**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain import PromptTemplate, LLMChain\n",
    "\n",
    "template = \"\"\"Question: {question}\n",
    "\n",
    "Answer:\"\"\"\n",
    "\n",
    "prompt = PromptTemplate(template=template, input_variables=['question'])\n",
    "\n",
    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
    "\n",
    "response = llm_chain.run('What is AI?')"
   ]
  }
 ],
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  "language_info": {
   "name": "python"
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