update READMEs

docs/inference.md

@@ -86,6 +86,8 @@ To run the 70B Instruct model example run the following, it asks for system and
 python code_instruct_example.py --model_name codellama/CodeLlama-70b-Instruct-hf
 
 ```
+You can learn more about the chat prompt template [on HF](https://huggingface.co/codellama/CodeLlama-70b-Instruct-hf#chat-prompt) and [original CodeLlama repository](https://github.com/facebookresearch/codellama/blob/main/README.md#fine-tuned-instruction-models). HF tokenizer has already taken care of the chat template as shown in this example. 
+
 ### Llama Guard
 
 Llama Guard is a new experimental model that provides input and output guardrails for LLM deployments. For more details, please visit the main [repository](https://github.com/facebookresearch/PurpleLlama/tree/main/Llama-Guard).

examples/README.md

@@ -24,7 +24,7 @@ So far, we have provide the following inference examples:
 
 4. A [chat completion](./chat_completion/chat_completion.py) example highlighting the handling of chat dialogs.
 
-5. [Code Llama](./code_llama/) folder which provides examples for [code completion](./code_llama/code_completion_example.py) and [code infilling](./code_llama/code_infilling_example.py).
+5. [Code Llama](./code_llama/) folder which provides examples for [code completion](./code_llama/code_completion_example.py), [code infilling](./code_llama/code_infilling_example.py) and [Llama2 70B code instruct](./code_llama/code_instruct_example.py).
 
 6. The [Purple Llama Using Anyscale](./Purple_Llama_Anyscale.ipynb) is a notebook that shows how to use Anyscale hosted Llama Guard model to classify user inputs as safe or unsafe.