update readme and delete stale content

recipes/inference/llama_web_ui/Llama2_Gradio.ipynb

@@ -1,130 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e4532411",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO REFACTOR: Integrate code from _legacy/inference.py into this notebook"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "47a9adb3",
-   "metadata": {},
-   "source": [
-    "## This demo app shows how to query Llama 2 using the Gradio UI.\n",
-    "\n",
-    "Since we are using Replicate in this example, you will need to replace `<your replicate api token>` with your API token.\n",
-    "\n",
-    "To get the Replicate token: \n",
-    "\n",
-    "- You will need to first sign in with Replicate with your github account\n",
-    "- Then create a free API token [here](https://replicate.com/account/api-tokens) that you can use for a while \n",
-    "\n",
-    "**Note** After the free trial ends, you will need to enter billing info to continue to use Llama2 hosted on Replicate.\n",
-    "\n",
-    "To run this example:\n",
-    "- Set up your Replicate API token and enter it in place of `<your replicate api token>`\n",
-    "- Run the notebook\n",
-    "- Enter your question and click Submit\n",
-    "\n",
-    "In the notebook or a browser with URL http://127.0.0.1:7860 you should see a UI with your answer."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "928041cc",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Init param `input` is deprecated, please use `model_kwargs` instead.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running on local URL:  http://127.0.0.1:7860\n",
-      "\n",
-      "To create a public link, set `share=True` in `launch()`.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": []
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.schema import AIMessage, HumanMessage\n",
-    "import gradio as gr\n",
-    "from langchain.llms import Replicate\n",
-    "import os\n",
-    "\n",
-    "os.environ[\"REPLICATE_API_TOKEN\"] = \"<your replicate api token>\"\n",
-    "\n",
-    "llama2_13b_chat = \"meta/llama-2-13b-chat:f4e2de70d66816a838a89eeeb621910adffb0dd0baba3976c96980970978018d\"\n",
-    "\n",
-    "llm = Replicate(\n",
-    "    model=llama2_13b_chat,\n",
-    "    model_kwargs={\"temperature\": 0.01, \"top_p\": 1, \"max_new_tokens\":500}\n",
-    ")\n",
-    "\n",
-    "\n",
-    "def predict(message, history):\n",
-    "    history_langchain_format = []\n",
-    "    for human, ai in history:\n",
-    "        history_langchain_format.append(HumanMessage(content=human))\n",
-    "        history_langchain_format.append(AIMessage(content=ai))\n",
-    "    history_langchain_format.append(HumanMessage(content=message))\n",
-    "    gpt_response = llm(message) #history_langchain_format)\n",
-    "    return gpt_response#.content\n",
-    "\n",
-    "gr.ChatInterface(predict).launch()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

recipes/inference/llama_web_ui/README.md

@@ -1,25 +0,0 @@
-## Quick Web UI for Llama2 Chat
-If you prefer to see Llama2 in action in a web UI, instead of the notebooks above, you can try one of the two methods:
-
-### Running [Streamlit](https://streamlit.io/) with Llama2
-Open a Terminal, run the following commands:
-```
-pip install streamlit langchain replicate
-git clone https://github.com/facebookresearch/llama-recipes
-cd llama-recipes/llama-demo-apps
-```
-
-Replace the `<your replicate api token>` in `streamlit_llama2.py` with your API token created [here](https://replicate.com/account/api-tokens) - for more info, see the note [above](#replicate_note).
-
-Then run the command `streamlit run streamlit_llama2.py` and you'll see on your browser the following UI with question and answer - you can enter new text question, click Submit, and see Llama2's answer:
-
-![](../../../docs/images/llama2-streamlit.png)
-![](../../../docs/images/llama2-streamlit2.png)
-
-### Running [Gradio](https://www.gradio.app/) with Llama2 (using [Replicate](Llama2_Gradio.ipynb) or [OctoAI](../../llama_api_providers/OctoAI_API_examples/Llama2_Gradio.ipynb))
-
-To see how to query Llama2 and get answers with the Gradio UI both from the notebook and web, just launch the notebook `Llama2_Gradio.ipynb`. For more info, on how to get set up with a token to power these apps, see the note on [Replicate](../../README.md#replicate_note) and [OctoAI](../../README.md##octoai_note).
-
-Then enter your question, click Submit. You'll see in the notebook or a browser with URL http://127.0.0.1:7860 the following UI:
-
-![](../../../docs/images/llama2-gradio.png)

recipes/inference/llama_web_ui/requirements.txt

@@ -1,3 +0,0 @@
-streamlit
-langchain
-replicate

recipes/inference/llama_web_ui/streamlit_llama2.py

@@ -1,27 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
-
-# TODO REFACTOR: Convert this to an ipynb notebook
-
-import streamlit as st
-from langchain.llms import Replicate
-import os
-
-st.title("Llama2-powered Streamlit App")
-
-with st.sidebar:
-    os.environ["REPLICATE_API_TOKEN"] = "<your replicate api token>"
-
-def generate_response(input_text):
-    llama2_13b_chat = "meta/llama-2-13b-chat:f4e2de70d66816a838a89eeeb621910adffb0dd0baba3976c96980970978018d"
-
-    llm = Replicate(
-        model=llama2_13b_chat,
-        model_kwargs={"temperature": 0.01, "top_p": 1, "max_new_tokens":500}
-    )
-    st.info(llm(input_text))
-
-with st.form("my_form"):
-    text = st.text_area("Enter text:", "What is Generative AI?")
-    submitted = st.form_submit_button("Submit")
-    generate_response(text)

recipes/inference/model_servers/README.md

@@ -1,4 +1,2 @@
-## [Running Llama2 On-Prem with vLLM and TGI](llama-on-prem.md)
-This tutorial shows how to use Llama 2 with [vLLM](https://github.com/vllm-project/vllm) and Hugging Face [TGI](https://github.com/huggingface/text-generation-inference) to build Llama 2 on-prem apps.
-
-\* To run a quantized Llama2 model on iOS and Android, you can use  the open source [MLC LLM](https://github.com/mlc-ai/mlc-llm) or [llama.cpp](https://github.com/ggerganov/llama.cpp). You can even make a Linux OS that boots to Llama2 ([repo](https://github.com/trholding/llama2.c)).
+## [Running Llama 3 On-Prem with vLLM and TGI](llama-on-prem.md)
+This tutorial shows how to use Llama 3 with [vLLM](https://github.com/vllm-project/vllm) and Hugging Face [TGI](https://github.com/huggingface/text-generation-inference) to build Llama 3 on-prem apps.