Create chatml.md (#238)

* Create chatml.md

* Update chatml.md

Author: logankilpatrick

chatml.md

@@ -0,0 +1,87 @@
+Traditionally, GPT models consumed unstructured text. ChatGPT models
+instead expect a structured format, called Chat Markup Language
+(ChatML for short).
+ChatML documents consists of a sequence of messages. Each message
+contains a header (which today consists of who said it, but in the
+future will contain other metadata) and contents (which today is a
+text payload, but in the future will contain other datatypes).
+We are still evolving ChatML, but the current version (ChatML v0) can
+be represented with our upcoming "list of dicts" JSON format as
+follows:
+```
+[
+ {"token": "<|im_start|>"},
+ "system\nYou are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible.\nKnowledge cutoff: 2021-09-01\nCurrent date: 2023-03-01",
+ {"token": "<|im_end|>"}, "\n", {"token": "<|im_start|>"},
+ "user\nHow are you",
+ {"token": "<|im_end|>"}, "\n", {"token": "<|im_start|>"},
+ "assistant\nI am doing well!",
+ {"token": "<|im_end|>"}, "\n", {"token": "<|im_start|>"},
+ "user\nHow are you now?",
+ {"token": "<|im_end|>"}, "\n"
+]
+```
+You could also represent it in the classic "unsafe raw string"
+format. Note this format inherently allows injections from user input
+containing special-token syntax, similar to a SQL injections:
+```
+<|im_start|>system
+You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible.
+Knowledge cutoff: 2021-09-01
+Current date: 2023-03-01<|im_end|>
+<|im_start|>user
+How are you<|im_end|>
+<|im_start|>assistant
+I am doing well!<|im_end|>
+<|im_start|>user
+How are you now?<|im_end|>
+```
+## Non-chat use-cases
+ChatML can be applied to classic GPT use-cases that are not
+traditionally thought of as chat. For example, instruction following
+(where a user requests for the AI to complete an instruction) can be
+implemented as a ChatML query like the following:
+```
+[
+ {"token": "<|im_start|>"},
+ "user\nList off some good ideas:",
+ {"token": "<|im_end|>"}, "\n", {"token": "<|im_start|>"},
+ "assistant"
+]
+```
+We do not currently allow autocompleting of partial messages, 
+```
+[
+ {"token": "<|im_start|>"},
+ "system\nPlease autocomplete the user's message."
+ {"token": "<|im_end|>"}, "\n", {"token": "<|im_start|>"},
+ "user\nThis morning I decided to eat a giant"
+]
+```
+Note that ChatML makes explicit to the model the source of each piece
+of text, and particularly shows the boundary between human and AI
+text. This gives an opportunity to mitigate and eventually solve
+injections, as the model can tell which instructions come from the
+developer, the user, or its own input.
+## Few-shot prompting
+In general, we recommend adding few-shot examples using separate
+`system` messages with a `name` field of `example_user` or
+`example_assistant`. For example, here is a 1-shot prompt:
+```
+<|im_start|>system
+Translate from English to French
+<|im_end|>
+<|im_start|>system name=example_user
+How are you?
+<|im_end|>
+<|im_start|>system name=example_assistant
+Comment allez-vous?
+<|im_end|>
+<|im_start|>user
+{{user input here}}<|im_end|>
+```
+If adding instructions in the `system` message doesn't work, you can
+also try putting them into a `user` message.  (In the near future, we
+will train our models to be much more steerable via the system
+message. But to date, we have trained only on a few system messages,
+so the models pay much most attention to user examples.)