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

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"# Huggingface TextGen Inference\n",
"\n",
"[Text Generation Inference](https://github.com/huggingface/text-generation-inference) is a Rust, Python and gRPC server for text generation inference. Used in production at [HuggingFace](https://huggingface.co/) to power LLMs api-inference widgets.\n",
"\n",
"This notebooks goes over how to use a self hosted LLM using `Text Generation Inference`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To use, you should have the `text_generation` python package installed."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# !pip3 install text_generation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain.llms import HuggingFaceTextGenInference\n",
"\n",
"llm = HuggingFaceTextGenInference(\n",
" inference_server_url=\"http://localhost:8010/\",\n",
" max_new_tokens=512,\n",
" top_k=10,\n",
" top_p=0.95,\n",
" typical_p=0.95,\n",
" temperature=0.01,\n",
" repetition_penalty=1.03,\n",
")\n",
"llm(\"What did foo say about bar?\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Streaming"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain.llms import HuggingFaceTextGenInference\n",
"from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
"\n",
"\n",
"llm = HuggingFaceTextGenInference(\n",
" inference_server_url=\"http://localhost:8010/\",\n",
" max_new_tokens=512,\n",
" top_k=10,\n",
" top_p=0.95,\n",
" typical_p=0.95,\n",
" temperature=0.01,\n",
" repetition_penalty=1.03,\n",
" streaming=True\n",
")\n",
"llm(\"What did foo say about bar?\", callbacks=[StreamingStdOutCallbackHandler()])"
]
}
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