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				 <h1 align="center"> Getting Started </h1> 
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				 <p align="center"> 
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				-	<a href="https://llama.developer.meta.com/join_waitlist?utm_source=llama-cookbook&utm_medium=readme&utm_campaign=getting_started"><img src="https://img.shields.io/badge/Llama_API-Join_Waitlist-brightgreen?logo=meta" /></a> 
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				-	<a href="https://llama.developer.meta.com/docs?utm_source=llama-cookbook&utm_medium=readme&utm_campaign=getting_started"><img src="https://img.shields.io/badge/Llama_API-Documentation-4BA9FE?logo=meta" /></a> 
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				+	<a href="https://llama.developer.meta.com/join_waitlist?utm_source=llama-cookbook&utm_medium=readme&utm_campaign=getting_started"><img alt="Llama API Join Waitlist" src="https://img.shields.io/badge/Llama_API-Join_Waitlist-brightgreen?logo=meta" /></a> 
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				+	<a href="https://llama.developer.meta.com/docs?utm_source=llama-cookbook&utm_medium=readme&utm_campaign=getting_started"><img alt="Llama API Documentation" src="https://img.shields.io/badge/Llama_API-Documentation-4BA9FE?logo=meta" /></a> 
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				 </p> 
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				 <p align="center"> 
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				 </p> 
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				 <p align="center"> 
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				-	<a href="https://github.com/meta-llama/synthetic-data-kit"><img alt="Llama Tools Syntethic Data Kit" src="https://img.shields.io/badge/Llama_Tools-synthetic--data--kit-orange?logo=meta" /></a> 
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				-	<a href="https://github.com/meta-llama/llama-prompt-ops"><img alt="Llama Tools Syntethic Data Kit" src="https://img.shields.io/badge/Llama_Tools-llama--prompt--ops-orange?logo=meta" /></a> 
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				+	<a href="https://github.com/meta-llama/synthetic-data-kit"><img alt="Llama Tools Synthetic Data Kit" src="https://img.shields.io/badge/Llama_Tools-synthetic--data--kit-orange?logo=meta" /></a> 
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				+	<a href="https://github.com/meta-llama/llama-prompt-ops"><img alt="Llama Tools Synthetic Data Kit" src="https://img.shields.io/badge/Llama_Tools-llama--prompt--ops-orange?logo=meta" /></a> 
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				 </p> 
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				 If you are new to developing with Meta Llama models, this is where you should start. This folder contains introductory-level notebooks across different techniques relating to Meta Llama. 
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				@@ -21,5 +21,5 @@ If you are new to developing with Meta Llama models, this is where you should st 
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				 * The [Build_with_Llama API](./build_with_llama_api.ipynb) notebook highlights some of the features of [Llama API](https://llama.developer.meta.com?utm_source=llama-cookbook&utm_medium=readme&utm_campaign=getting_started). 
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				 * The [inference](./inference/) folder contains scripts to deploy Llama for inference on server and mobile. See also [3p_integrations/vllm](../3p-integrations/vllm/) and [3p_integrations/tgi](../3p-integrations/tgi/) for hosting Llama on open-source model servers. 
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				 * The [RAG](./RAG/) folder contains a simple Retrieval-Augmented Generation application using Llama. 
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				-* The [finetuning](./finetuning/) folder contains resources to help you finetune Llama on your custom datasets, for both single- and multi-GPU setups. The scripts use the native llama-cookbook finetuning code found in [finetuning.py](../src/llama_cookbook/finetuning.py) which supports these features: 
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				+* The [finetuning](./finetuning/) folder contains resources to help you finetune Llama on your custom datasets, for both single- and multi-GPU setups. The scripts use the native llama-cookbook finetuning code found in [finetuning.py](../src/llama_cookbook/finetuning.py) which supports these features. 
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				 * The [llama-tools](./llama-tools/) folder contains resources to help you use Llama tools, such as [llama-prompt-ops](../llama-tools/llama-prompt-ops_101.ipynb). 
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