Update links in README.md (meta-llama#589)

Author: subramen

recipes/quickstart/README.md

@@ -2,11 +2,11 @@
 
 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.
 
-* 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_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:
+* The [Running_Llama3_Anywhere](./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](./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](./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.
+* The [RAG](./RAG/) folder contains a simple Retrieval-Augmented Generation application using Llama 3.
+* The [finetuning](./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 [finetuning.py](../../src/llama_recipes/finetuning.py) which supports these features:
 
 | Feature                                        |   |
 | ---------------------------------------------- | - |