mirror of
https://github.com/hwchase17/langchain
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110 lines
2.7 KiB
Plaintext
110 lines
2.7 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Huggingface TextGen Inference\n",
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"\n",
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"[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",
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"\n",
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"This notebooks goes over how to use a self hosted LLM using `Text Generation Inference`."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"To use, you should have the `text_generation` python package installed."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# !pip3 install text_generation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.llms import HuggingFaceTextGenInference\n",
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"\n",
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"llm = HuggingFaceTextGenInference(\n",
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" inference_server_url=\"http://localhost:8010/\",\n",
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" max_new_tokens=512,\n",
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" top_k=10,\n",
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" top_p=0.95,\n",
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" typical_p=0.95,\n",
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" temperature=0.01,\n",
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" repetition_penalty=1.03,\n",
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")\n",
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"llm(\"What did foo say about bar?\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Streaming"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.llms import HuggingFaceTextGenInference\n",
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"from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
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"\n",
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"\n",
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"llm = HuggingFaceTextGenInference(\n",
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" inference_server_url=\"http://localhost:8010/\",\n",
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" max_new_tokens=512,\n",
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" top_k=10,\n",
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" top_p=0.95,\n",
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" typical_p=0.95,\n",
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" temperature=0.01,\n",
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" repetition_penalty=1.03,\n",
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" stream=True\n",
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")\n",
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"llm(\"What did foo say about bar?\", callbacks=[StreamingStdOutCallbackHandler()])"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.3"
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},
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"vscode": {
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"interpreter": {
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"hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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