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    "# DeepSparse\n",
    "\n",
    "This page covers how to use the [DeepSparse](https://github.com/neuralmagic/deepsparse) inference runtime within LangChain.\n",
    "It is broken into two parts: installation and setup, and then examples of DeepSparse usage.\n",
    "\n",
    "## Installation and Setup\n",
    "\n",
    "- Install the Python package with `pip install deepsparse`\n",
    "- Choose a [SparseZoo model](https://sparsezoo.neuralmagic.com/?useCase=text_generation) or export a support model to ONNX [using Optimum](https://github.com/neuralmagic/notebooks/blob/main/notebooks/opt-text-generation-deepsparse-quickstart/OPT_Text_Generation_DeepSparse_Quickstart.ipynb)\n",
    "\n",
    "\n",
    "There exists a DeepSparse LLM wrapper, that provides a unified interface for all models:"
   ]
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   "source": [
    "from langchain.llms import DeepSparse\n",
    "\n",
    "llm = DeepSparse(model='zoo:nlg/text_generation/codegen_mono-350m/pytorch/huggingface/bigpython_bigquery_thepile/base-none')\n",
    "\n",
    "print(llm('def fib():'))"
   ]
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    "Additional parameters can be passed using the `config` parameter:"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "ff61b845-41e6-4457-8625-6e21a11bfe7c",
   "metadata": {},
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   "source": [
    "config = {'max_generated_tokens': 256}\n",
    "\n",
    "llm = DeepSparse(model='zoo:nlg/text_generation/codegen_mono-350m/pytorch/huggingface/bigpython_bigquery_thepile/base-none', config=config)"
   ]
  }
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