langchain/docs/extras/integrations/llms/titan_takeoff.ipynb

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    "# Titan Takeoff\n",
    "\n",
    "TitanML helps businesses build and deploy better, smaller, cheaper, and faster NLP models through our training, compression, and inference optimization platform. \n",
    "\n",
    "Our inference server, [Titan Takeoff](https://docs.titanml.co/docs/titan-takeoff/getting-started) enables deployment of LLMs locally on your hardware in a single command. Most generative model architectures are supported, such as Falcon, Llama 2, GPT2, T5 and many more."
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   "source": [
    "## Installation\n",
    "\n",
    "To get started with Iris Takeoff, all you need is to have docker and python installed on your local system. If you wish to use the server with gpu suport, then you will need to install docker with cuda support.\n",
    "\n",
    "For Mac and Windows users, make sure you have the docker daemon running! You can check this by running docker ps in your terminal. To start the daemon, open the docker desktop app.\n",
    "\n",
    "Run the following command to install the Iris CLI that will enable you to run the takeoff server:"
   ]
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    "pip install titan-iris"
   ]
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   "source": [
    "## Choose a Model\n",
    "Iris Takeoff supports many of the most powerful generative text models, such as Falcon, MPT, and Llama. See the [supported models](https://docs.titanml.co/docs/titan-takeoff/supported-models) for more information. For information about using your own models, see the [custom models](https://docs.titanml.co/docs/titan-takeoff/Advanced/custom-models).\n",
    "\n",
    "Going forward in this demo we will be using the falcon 7B instruct model. This is a good open source model that is trained to follow instructions, and is small enough to easily inference even on CPUs.\n",
    "\n",
    "## Taking off\n",
    "Models are referred to by their model id on HuggingFace. Takeoff uses port 8000 by default, but can be configured to use another port. There is also support to use a Nvidia GPU by specifing cuda for the device flag.\n",
    "\n",
    "To start the takeoff server, run:"
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    "iris takeoff --model tiiuae/falcon-7b-instruct --device cpu\n",
    "iris takeoff --model tiiuae/falcon-7b-instruct --device cuda # Nvidia GPU required\n",
    "iris takeoff --model tiiuae/falcon-7b-instruct --device cpu --port 5000 # run on port 5000 (default: 8000)\n",
    "```"
   ]
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   "source": [
    "You will then be directed to a login page, where you will need to create an account to proceed.\n",
    "After logging in, run the command onscreen to check whether the server is ready. When it is ready, you can start using the Takeoff integration\n",
    "\n",
    "## Inferencing your model\n",
    "To access your LLM, use the TitanTakeoff LLM wrapper:"
   ]
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  {
   "cell_type": "code",
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   "source": [
    "from langchain.llms import TitanTakeoff\n",
    "\n",
    "llm = TitanTakeoff(\n",
    "    port=8000,\n",
    "    generate_max_length=128,\n",
    "    temperature=1.0\n",
    ")\n",
    "\n",
    "prompt = \"What is the largest planet in the solar system?\"\n",
    "\n",
    "llm(prompt)"
   ]
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    "No parameters are needed by default, but a port can be specified and [generation parameters](https://docs.titanml.co/docs/titan-takeoff/Advanced/generation-parameters) can be supplied.\n",
    "\n",
    "### Streaming\n",
    "Streaming is also supported via the streaming flag:"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
    "from langchain.callbacks.manager import CallbackManager\n",
    "\n",
    "llm = TitanTakeoff(port=8000, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]), streaming=True)\n",
    "\n",
    "prompt = \"What is the capital of France?\"\n",
    "\n",
    "llm(prompt)"
   ]
  },
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   "source": [
    "### Integration with LLMChain"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "from langchain import PromptTemplate, LLMChain\n",
    "\n",
    "llm = TitanTakeoff()\n",
    "\n",
    "template = \"What is the capital of {country}\"\n",
    "\n",
    "prompt = PromptTemplate(template=template, input_variables=[\"country\"])\n",
    "\n",
    "llm_chain = LLMChain(llm=llm, prompt=prompt)\n",
    "\n",
    "generated = llm_chain.run(country=\"Belgium\")\n",
    "print(generated)"
   ]
  }
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