diff --git a/README.md b/README.md index 6e48daa8410a1d97d9a4aea2e1c32761b67120d0..918b5c51349ff01e6d84d4d3ec07be379833fd44 100644 --- a/README.md +++ b/README.md @@ -137,7 +137,7 @@ Contains examples are organized in folders by topic: |---|---| [quickstart](./recipes/quickstart) | The "Hello World" of using Llama, start here if you are new to using Llama. [use_cases](./recipes/use_cases)|Scripts showing common applications of Meta Llama3 -[3p_integration](./recipes/3p_integration)|Partner owned folder showing common applications of Meta Llama3 +[3p_integrations](./recipes/3p_integrations)|Partner owned folder showing common applications of Meta Llama3 [responsible_ai](./recipes/responsible_ai)|Scripts to use PurpleLlama for safeguarding model outputs ### `src/` diff --git a/recipes/3p_integration/README.md b/recipes/3p_integrations/README.md similarity index 100% rename from recipes/3p_integration/README.md rename to recipes/3p_integrations/README.md diff --git a/recipes/3p_integration/aws/getting_started_llama_3_on_amazon_bedrock.ipynb b/recipes/3p_integrations/aws/getting_started_llama_3_on_amazon_bedrock.ipynb similarity index 100% rename from recipes/3p_integration/aws/getting_started_llama_3_on_amazon_bedrock.ipynb rename to recipes/3p_integrations/aws/getting_started_llama_3_on_amazon_bedrock.ipynb diff --git 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a/recipes/3p_integration/tgi/merge_lora_weights.py b/recipes/3p_integrations/tgi/merge_lora_weights.py similarity index 100% rename from recipes/3p_integration/tgi/merge_lora_weights.py rename to recipes/3p_integrations/tgi/merge_lora_weights.py diff --git a/recipes/3p_integration/using_externally_hosted_llms.ipynb b/recipes/3p_integrations/using_externally_hosted_llms.ipynb similarity index 100% rename from recipes/3p_integration/using_externally_hosted_llms.ipynb rename to recipes/3p_integrations/using_externally_hosted_llms.ipynb diff --git a/recipes/3p_integration/vllm/inference.py b/recipes/3p_integrations/vllm/inference.py similarity index 100% rename from recipes/3p_integration/vllm/inference.py rename to recipes/3p_integrations/vllm/inference.py diff --git a/recipes/README.md b/recipes/README.md index e0e6437c75690753440cbdbb7ea5c65746829f52..d0a4bbc74b1368c829a360c029ea5df9aaaac152 100644 --- a/recipes/README.md +++ b/recipes/README.md @@ -4,5 +4,5 @@ This folder contains examples organized by topic: |---|---| [quickstart](./quickstart)|The "Hello World" of using Llama 3, start here if you are new to using Llama 3 [use_cases](./use_cases)|Scripts showing common applications of Llama 3 -[3p_integration](./3p_integration)|Partner owned folder showing common applications of Meta Llama3 +[3p_integrations](./3p_integrations)|Partner owned folder showing common applications of Meta Llama3 [responsible_ai](./responsible_ai)|Scripts to use PurpleLlama for safeguarding model outputs diff --git a/recipes/quickstart/README.md b/recipes/quickstart/README.md index 315ab70a8b630c049a8a063b906d5cefd433c464..429d1680fe8fe8ff5f70d4887820b6396b78f5e4 100644 --- a/recipes/quickstart/README.md +++ b/recipes/quickstart/README.md @@ -4,7 +4,7 @@ If you are new to developing with Meta Llama models, this is where you should st * The [](./Running_Llama3_Anywhere/) notebooks demonstrate how to run Llama inference across Linux, Mac and Windows platforms using the appropriate tooling. * The [](./Prompt_Engineering_with_Llama_3.ipynb) notebook showcases the various ways to elicit appropriate outputs from Llama. Take this notebook for a spin to get a feel for how Llama responds to different inputs and generation parameters. -* The [](./inference/) folder contains scripts to deploy Llama for inference on server and mobile. See also [](../3p_integration/vllm/) and [](../3p_integration/tgi/) for hosting Llama on open-source model servers. +* The [](./inference/) folder contains scripts to deploy Llama for inference on server and mobile. See also [](../3p_integrations/vllm/) and [](../3p_integrations/tgi/) for hosting Llama on open-source model servers. * The [](./RAG/) folder contains a simple Retrieval-Augmented Generation application using Llama 3. * The [](./finetuning/) folder contains resources to help you finetune Llama 3 on your custom datasets, for both single- and multi-GPU setups. The scripts use the native llama-recipes finetuning code found in [](../../src/llama_recipes/finetuning.py) which supports these features: diff --git a/recipes/quickstart/inference/mobile_inference/android_inference/README.md b/recipes/quickstart/inference/mobile_inference/android_inference/README.md index e310c8aaa99bd7bd153d99ea2a2ca5a9a250c87b..5a0ec16b575550c6cba54ce3fa336f9b91e340bc 100644 --- a/recipes/quickstart/inference/mobile_inference/android_inference/README.md +++ b/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](../../../../3p_integration/octoai/). +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_integrations/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 diff --git a/recipes/use_cases/README.md b/recipes/use_cases/README.md index 8922774ae0e68afffa090d66074b1f96f034e57e..fa23b8cfda54a7c3612a9e7e1e189ebe2585b7b9 100644 --- a/recipes/use_cases/README.md +++ b/recipes/use_cases/README.md @@ -1,10 +1,10 @@ -## [VideoSummary](video_summary.ipynb): Ask Llama 3 to Summarize a Long YouTube Video (using Replicate or [OctoAI](../3p_integration/octoai/video_summary.ipynb)) +## [VideoSummary](video_summary.ipynb): Ask Llama 3 to Summarize a Long YouTube Video (using Replicate or [OctoAI](../3p_integrations/octoai/video_summary.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](./coding/text2sql/structured_llama.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. -## [live_data](live_data.ipynb): Ask Llama 3 about Live Data (using Replicate or [OctoAI](../3p_integration/octoai/live_data.ipynb)) +## [live_data](live_data.ipynb): Ask Llama 3 about Live Data (using Replicate or [OctoAI](../3p_integrations/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](./customerservice_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](./customerservice_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](./customerservice_chatbots/RAG_chatbot/RAG_Chatbot_Example.ipynb) or on [OctoAI](../3p_integration/octoai/RAG_chatbot_example/RAG_chatbot_example.ipynb)) +### RAG Chatbot Example (running [locally](./customerservice_chatbots/RAG_chatbot/RAG_Chatbot_Example.ipynb) or on [OctoAI](../3p_integrations/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](./customerservice_chatbots/sales_bot/SalesBot.ipynb): Sales Bot with Llama3 - A Summarization and RAG Use Case diff --git a/tools/benchmarks/inference/on_prem/README.md b/tools/benchmarks/inference/on_prem/README.md index 747ca47b6a37fbe7a840da8c0030a8f7a6d31033..f29c8f8179eea7d1b23899ec137159b261bc1db6 100644 --- a/tools/benchmarks/inference/on_prem/README.md +++ b/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](../../../../recipes/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_integrations/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: