langchain/docs/versioned_docs/version-0.2.x/integrations/llms/ctransformers.ipynb

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   "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 --upgrade --quiet  ctransformers"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Load Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.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(\n",
    "    model=\"marella/gpt-2-ggml\", callbacks=[StreamingStdOutCallbackHandler()]\n",
    ")\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.chains import LLMChain\n",
    "from langchain_core.prompts import PromptTemplate\n",
    "\n",
    "template = \"\"\"Question: {question}\n",
    "\n",
    "Answer:\"\"\"\n",
    "\n",
    "prompt = PromptTemplate.from_template(template)\n",
    "\n",
    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
    "\n",
    "response = llm_chain.run(\"What is AI?\")"
   ]
  }
 ],
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