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

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   "source": [
    "# MosaicML\n",
    "\n",
    "[MosaicML](https://docs.mosaicml.com/en/latest/inference.html) offers a managed inference service. You can either use a variety of open-source models, or deploy your own.\n",
    "\n",
    "This example goes over how to use LangChain to interact with MosaicML Inference for text completion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sign up for an account: https://forms.mosaicml.com/demo?utm_source=langchain\n",
    "\n",
    "from getpass import getpass\n",
    "\n",
    "MOSAICML_API_TOKEN = getpass()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.environ[\"MOSAICML_API_TOKEN\"] = MOSAICML_API_TOKEN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.chains import LLMChain\n",
    "from langchain_community.llms import MosaicML\n",
    "from langchain_core.prompts import PromptTemplate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "template = \"\"\"Question: {question}\"\"\"\n",
    "\n",
    "prompt = PromptTemplate.from_template(template)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "llm = MosaicML(inject_instruction_format=True, model_kwargs={\"max_new_tokens\": 128})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "llm_chain = LLMChain(prompt=prompt, llm=llm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "question = \"What is one good reason why you should train a large language model on domain specific data?\"\n",
    "\n",
    "llm_chain.run(question)"
   ]
  }
 ],
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