3p_integration folder updates

recipes/3p_integration/README.md

@@ -1,2 +1,2 @@
-## [Running Llama 3 On-Prem with vLLM and TGI](llama-on-prem.md)
+## [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.

recipes/3p_integration/lamini/text2sql_memory_tuning/README.md

@@ -1,10 +1,10 @@
 # Tune Llama 3 for text-to-SQL and improve accuracy from 30% to 95%
 
-This repo and notebook `meta-lamini.ipynb` demonstrate how to tune Llama 3 to generate valid SQL queries and improve accuracy from 30% to 95%.
+This repo and notebook `meta_lamini.ipynb` demonstrate how to tune Llama 3 to generate valid SQL queries and improve accuracy from 30% to 95%.
 
-In this notebook we'll be using Lamini, and more specifically, Lamini Memory Tuning. 
+In this notebook we'll be using Lamini, and more specifically, Lamini Memory Tuning.
 
-Lamini is an integrated platform for LLM inference and tuning for the enterprise. Lamini Memory Tuning is a new tool you can use to embed facts into LLMs that improves factual accuracy and reduces hallucinations. Inspired by information retrieval, this method has set a new standard of accuracy for LLMs with less developer effort. 
+Lamini is an integrated platform for LLM inference and tuning for the enterprise. Lamini Memory Tuning is a new tool you can use to embed facts into LLMs that improves factual accuracy and reduces hallucinations. Inspired by information retrieval, this method has set a new standard of accuracy for LLMs with less developer effort.
 
 Learn more about Lamini Memory Tuning: https://www.lamini.ai/blog/lamini-memory-tuning
 

recipes/3p_integration/hf_text_generation_inference/README.md

@@ -9,7 +9,7 @@ In case the model was fine tuned with LoRA method we need to merge the weights o
 The script takes the base model, the peft weight folder as well as an output as arguments:
 
 ```
-python -m llama_recipes.inference.hf_text_generation_inference.merge_lora_weights --base_model llama-7B --peft_model ft_output --output_dir data/merged_model_output
+python -m llama_recipes.recipes.3p_integration.tgi.merge_lora_weights --base_model llama-7B --peft_model ft_output --output_dir data/merged_model_output
 ```
 
 ## Step 1: Serving the model

recipes/quickstart/inference/mobile_inference/android_inference/README.md

@@ -9,7 +9,7 @@ Machine Learning Compilation for Large Language Models (MLC LLM) is a high-perfo
 
 You can read more about MLC-LLM at the following [link](https://github.com/mlc-ai/mlc-llm).
 
-MLC-LLM is also what powers the Llama3 inference APIs provided by [OctoAI](https://octo.ai/). You can use OctoAI for your Llama3 cloud-based inference needs by trying out the examples under the [following path](../../../../llama_api_providers/OctoAI_API_examples/).
+MLC-LLM is also what powers the Llama3 inference APIs provided by [OctoAI](https://octo.ai/). You can use OctoAI for your Llama3 cloud-based inference needs by trying out the examples under the [following path](../../../../3p_integration/octoai/).
 
 This tutorial was tested with the following setup:
 * MacBook Pro 16 inch from 2021 with Apple M1 Max and 32GB of RAM running Sonoma 14.3.1

recipes/use_cases/README.md

@@ -1,10 +1,10 @@
-## [VideoSummary](VideoSummary.ipynb): Ask Llama 3 to Summarize a Long YouTube Video (using Replicate or [OctoAI](../llama_api_providers/OctoAI_API_examples/VideoSummary.ipynb))
+## [VideoSummary](VideoSummary.ipynb): Ask Llama 3 to Summarize a Long YouTube Video (using Replicate or [OctoAI](../3p_integration/octoai/VideoSummary.ipynb))
 This demo app uses Llama 3 to return a text summary of a YouTube video. It shows how to retrieve the caption of a YouTube video and how to ask Llama to summarize the content in different ways, from the simplest naive way that works for short text to more advanced methods of using LangChain's map_reduce and refine to overcome the 8K context length limit of Llama 3.
 
 ## [NBA2023-24](./text2sql/StructuredLlama.ipynb): Ask Llama 3 about Structured Data
 This demo app shows how to use LangChain and Llama 3 to let users ask questions about **structured** data stored in a SQL DB. As the 2023-24 NBA season is entering the playoff, we use the NBA roster info saved in a SQLite DB to show you how to ask Llama 3 questions about your favorite teams or players.
 
-## [LiveData](LiveData.ipynb): Ask Llama 3 about Live Data (using Replicate or [OctoAI](../llama_api_providers/OctoAI_API_examples/LiveData.ipynb))
+## [live_data](live_data.ipynb): Ask Llama 3 about Live Data (using Replicate or [OctoAI](../3p_integration/octoai/live_data.ipynb))
 This demo app shows how to perform live data augmented generation tasks with Llama 3, [LlamaIndex](https://github.com/run-llama/llama_index), another leading open-source framework for building LLM apps, and the [Tavily](https://tavily.com) live search API.
 
 ## [WhatsApp Chatbot](./chatbots/whatsapp_llama/whatsapp_llama3.md): Building a Llama 3 Enabled WhatsApp Chatbot
@@ -13,7 +13,7 @@ This step-by-step tutorial shows how to use the [WhatsApp Business API](https://
 ## [Messenger Chatbot](./chatbots/messenger_llama/messenger_llama3.md): Building a Llama 3 Enabled Messenger Chatbot
 This step-by-step tutorial shows how to use the [Messenger Platform](https://developers.facebook.com/docs/messenger-platform/overview) to build a Llama 3 enabled Messenger chatbot.
 
-### RAG Chatbot Example (running [locally](./chatbots/RAG_chatbot/RAG_Chatbot_Example.ipynb) or on [OctoAI](../llama_api_providers/OctoAI_API_examples/RAG_Chatbot_example/RAG_Chatbot_Example.ipynb))
+### RAG Chatbot Example (running [locally](./chatbots/RAG_chatbot/RAG_Chatbot_Example.ipynb) or on [OctoAI](../3p_integration/octoai/RAG_Chatbot_example/RAG_Chatbot_Example.ipynb))
 A complete example of how to build a Llama 3 chatbot hosted on your browser that can answer questions based on your own data using retrieval augmented generation (RAG). You can run Llama2 locally if you have a good enough GPU or on OctoAI if you follow the note [here](../README.md#octoai_note).
 
 ## [Sales Bot](./chatbots/sales_bot/SalesBot.ipynb): Sales Bot with Llama3 - A Summarization and RAG Use Case

tools/benchmarks/inference/on_prem/README.md

@@ -7,7 +7,7 @@ We support benchmark on these serving framework:
 
 # vLLM - Getting Started
 
-To get started, we first need to deploy containers on-prem as a API host. Follow the guidance [here](../../../3p_integration/llama-on-prem.md#setting-up-vllm-with-llama-3) to deploy vLLM on-prem.
+To get started, we first need to deploy containers on-prem as a API host. Follow the guidance [here](../../../../recipes/3p_integration/llama_on_prem.md#setting-up-vllm-with-llama-3) to deploy vLLM on-prem.
 
 Note that in common scenario which overall throughput is important, we suggest you prioritize deploying as many model replicas as possible to reach higher overall throughput and request-per-second (RPS), comparing to deploy one model container among multiple GPUs for model parallelism. Additionally, as deploying multiple model replicas, there is a need for a higher level wrapper to handle the load balancing which here has been simulated in the benchmark scripts.
 For example, we have an instance from Azure that has 8xA100 80G GPUs, and we want to deploy the Meta Llama 3 70B instruct model, which is around 140GB with FP16. So for deployment we can do: