two readmes final updates; added words to spell checker list

README.md

@@ -1,6 +1,6 @@
 # Llama 2 Fine-tuning / Inference Recipes and Examples
 
-The 'llama-recipes' repository is a companion to the [Llama 2 model](https://github.com/facebookresearch/llama). The goal of this repository is to provide examples to quickly get started with fine-tuning for domain adaptation and how to run inference for the fine-tuned models. For ease of use, the examples use Hugging Face converted versions of the models. See steps for conversion of the model [here](#model-conversion-to-hugging-face).
+The 'llama-recipes' repository is a companion to the [Llama 2 model](https://github.com/facebookresearch/llama). The goal of this repository is to provide examples and demo apps to quickly get started with fine-tuning for domain adaptation and how to run inference for the fine-tuned models. For ease of use, the examples use Hugging Face converted versions of the models. See steps for conversion of the model [here](#model-conversion-to-hugging-face).
 
 Llama 2 is a new technology that carries potential risks with use. Testing conducted to date has not — and could not — cover all scenarios. In order to help developers address these risks, we have created the [Responsible Use Guide](https://github.com/facebookresearch/llama/blob/main/Responsible-Use-Guide.pdf). More details can be found in our research paper as well. For downloading the models, follow the instructions on [Llama 2 repo](https://github.com/facebookresearch/llama).
 
@@ -13,8 +13,9 @@ Llama 2 is a new technology that carries potential risks with use. Testing condu
     - [Multi GPU One Node](#multiple-gpus-one-node)
     - [Multi GPU Multi Node](#multi-gpu-multi-node)
 4. [Inference](./docs/inference.md)
-5. [Repository Organization](#repository-organization)
-6. [License and Acceptable Use Policy](#license)
+5. [Demo Apps](#demo-apps)
+6. [Repository Organization](#repository-organization)
+7. [License and Acceptable Use Policy](#license)
 
 
 
@@ -174,6 +175,17 @@ sbatch multi_node.slurm
 ```
 You can read more about our fine-tuning strategies [here](./docs/LLM_finetuning.md).
 
+# Demo Apps
+This folder contains a series of Llama2-powered apps:
+* Quickstart Llama deployments and basic interactions with Llama
+1. Llama on your Mac and ask Llama general questions
+2. Llama on Google Colab
+3. Llama on Cloud and ask Llama questions about unstructured data in a PDF
+
+* Specialized Llama use cases:
+1. Ask Llama to summarize a video content
+2. Ask Llama questions about structured data in a DB
+3. Ask Llama questions about live data on the web
 
 # Repository Organization
 This repository is organized in the following way:
@@ -184,6 +196,8 @@ This repository is organized in the following way:
 
 [datasets](src/llama_recipes/datasets/): Contains individual scripts for each dataset to download and process. Note: Use of any of the datasets should be in compliance with the dataset's underlying licenses (including but not limited to non-commercial uses)
 
+[demo_apps](./demo_apps) contains a series of Llama2-powered apps, from quickstart deployments to how to ask Llama questions about unstructured data, structured data, live data, and video summary.
+
 [examples](./examples/): Contains examples script for finetuning and inference of the Llama 2 model as well as how to use them safely.
 
 [inference](src/llama_recipes/inference/): Includes modules for inference for the fine-tuned models.

llama-demo-apps/README.md

@@ -1,6 +1,17 @@
 # Llama2 Demo Apps 
 
-This folder showcases Llama2-powered demo apps. If you need a general understanding of GenAI, Llama2, prompt engineering and RAG, be sure to first check the [Getting to know Llama 2 notebook](https://github.com/facebookresearch/llama-recipes/blob/main/examples/Getting_to_know_Llama.ipynb) and its Meta Connect video [here](https://www.facebook.com/watch/?v=662153709222699).
+This folder contains a series of Llama2-powered apps:
+* Quickstart Llama deployments and basic interactions with Llama
+1. Llama on your Mac and ask Llama general questions
+2. Llama on Google Colab
+3. Llama on Cloud and ask Llama questions about unstructured data in a PDF
+
+* Specialized Llama use cases:
+1. Ask Llama to summarize a video content
+2. Ask Llama questions about structured data in a DB
+3. Ask Llama questions about live data on the web
+
+If you need a general understanding of GenAI, Llama2, prompt engineering and RAG (Retrieval Augmented Generation), be sure to first check the [Getting to know Llama 2 notebook](https://github.com/facebookresearch/llama-recipes/blob/main/examples/Getting_to_know_Llama.ipynb) and its Meta Connect video [here](https://www.facebook.com/watch/?v=662153709222699).
 
 We start with three quickstart demos showing how to run Llama2 locally on a Mac, remotely in the cloud, and on a Google Colab to ask Llama2 general questions or questions about unstructured data not trained for the model.
 
@@ -30,7 +41,7 @@ You can also upload the notebooks to Google Colab.
 The first three demo apps show:
 * how to run Llama2 locally on a Mac, in the Google Colab notebook, and in the cloud using Replicate;
 * how to use [LangChain](https://github.com/langchain-ai/langchain), an open-source framework for building LLM apps, to ask Llama general questions in different ways;
-* how to use LangChain to load a recent PDF doc - the Llama2 paper pdf - and ask questions about it. This is the well known RAG (Retrieval Augmented Generation) method to let LLM such as Llama2 be able to answer questions about the data not publicly available when Llama2 was trained, or about your own data. RAG is one way to prevent LLM's hallucination. 
+* how to use LangChain to load a recent PDF doc - the Llama2 paper pdf - and ask questions about it. This is the well known RAG method to let LLM such as Llama2 be able to answer questions about the data not publicly available when Llama2 was trained, or about your own data. RAG is one way to prevent LLM's hallucination. 
 * how to ask follow up questions to Llama by sending previous questions and answers as the context along with the new question, hence performing multi-turn chat or conversation with Llama.
 
 ### [Running Llama2 Locally on Mac](HelloLlamaLocal.ipynb)

scripts/spellcheck_conf/wordlist.txt

@@ -1156,4 +1156,30 @@ Autocast
 FN
 GBs
 MLP
-learnable
+learnable
+Colab
+GenAI
+Gradio
+HelloLlama
+HelloLlamaCloud
+HelloLlamaLocal
+LLM's
+LangChain
+LangChain's
+LiveData
+LlamaIndex
+MBP
+MLC
+Replicate's
+StructuredLlama
+VideoSummary
+cpp
+envinronment
+ggml
+gguf
+gradio
+minnutes
+pdf
+quantized
+serarch
+streamlit