tocmo0nlord/axolotl
1
0
Fork 0
Code Issues Pull Requests Actions 3 Packages Projects Releases Wiki Activity

apply black formatting

Browse Source
This commit is contained in:
Wing Lian
2023-05-24 22:59:33 -04:00
parent ce694e20a3
commit ce34d64e8a
10 changed files with 248 additions and 108 deletions
Download Patch File Download Diff File Expand all files Collapse all files

63
src/axolotl/prompters.py
View File

@@ -23,12 +23,22 @@ class AlpacaPrompter:
def match_prompt_style(self):
if self.prompt_style == PromptStyle.instruct.value:
self.prompt_input = self.system_prompt + "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
self.prompt_no_input = self.system_no_input_prompt + "### Instruction:\n{instruction}\n\n### Response:\n"
self.prompt_input = (
self.system_prompt
+ "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
)
self.prompt_no_input = (
self.system_no_input_prompt
+ "### Instruction:\n{instruction}\n\n### Response:\n"
)
self.response_split = "### Response:"
if self.prompt_style == PromptStyle.chat.value:
self.prompt_input = self.system_prompt + "USER: {instruction}\n{input}\nASSISTANT:"
self.prompt_no_input = self.system_no_input_prompt + "USER: {instruction}\nASSISTANT:"
self.prompt_input = (
self.system_prompt + "USER: {instruction}\n{input}\nASSISTANT:"
)
self.prompt_no_input = (
self.system_no_input_prompt + "USER: {instruction}\nASSISTANT:"
)
self.response_split = "ASSISTANT:"
def build_prompt(
@@ -55,12 +65,15 @@ class UnpromptedPrompter(AlpacaPrompter):
system_prompt = ""
system_no_input_prompt = ""
class JeopardyPrompter(AlpacaPrompter):
prompt_input = "Below is a Jeopardy clue paired with input providing the category of the clue. Write a concise response that best answers tbe clue given the category.\n\n### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
class MultipleChoiceExplainPrompter(AlpacaPrompter):
system_prompt = "Choose the answer that best answers the question. Explain your reasoning."
system_prompt = (
"Choose the answer that best answers the question. Explain your reasoning."
)
class MultipleChoiceConcisePrompter(AlpacaPrompter):
@@ -68,11 +81,15 @@ class MultipleChoiceConcisePrompter(AlpacaPrompter):
class SummarizeTLDRPrompter(AlpacaPrompter):
prompt_no_input = "USER: Summarize the following article as a TL;DR.\n{instruction}\nASSISTANT:"
prompt_no_input = (
"USER: Summarize the following article as a TL;DR.\n{instruction}\nASSISTANT:"
)
class CompletionPrompter(AlpacaPrompter):
def build_prompt(self, instruction: str, input=None, output=None) -> Generator[str, None, None]:
def build_prompt(
self, instruction: str, input=None, output=None
) -> Generator[str, None, None]:
yield instruction
def get_response(self, output: str) -> str:
@@ -91,7 +108,9 @@ class ReflectAlpacaPrompter:
system_prompt = "Below is an instruction that describes a task, paired with an input that provides further context. You, the Assistant, should generate a response as if it were an abstract for an academic or technical paper on the query along with a methodology. Then generate an Agent Reflection where you create a long form response as if from subject matter expert, be verbose, diligent, and creative in your application of knowledge, apply it through the lens of the response generated by the assistant. Look for flawed reasoning, faulty logic, or other mistakes in the method. Finally, generate a final response and method for the user with the Assistant abstract and Reflection analysis as augmentations to the generation\n\n"
system_no_input_prompt = "Below is an instruction that describes a task. You, the Assistant, should generate a response as if it were an abstract for an academic or technical paper on the query along with a methodology. Then generate an Agent Reflection where you create a long form response as if from subject matter expert, be verbose, diligent, and creative in your application of knowledge, apply it through the lens of the response generated by the assistant. Look for flawed reasoning, faulty logic, or other mistakes in the method. Finally, generate a final response and method for the user with the Assistant abstract and Reflection analysis as augmentations to the generation\n\n"
prompt_input = "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
prompt_input = (
"### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
)
prompt_no_input = "### Instruction:\n{instruction}\n\n### Response:\n"
agent_label = "### Thought:\n{output}\n\n### Agent Reflection:\n{reflection}\n\n### Final Response:\n{corrected}"
response_split = "### Response:"
@@ -102,14 +121,26 @@ class ReflectAlpacaPrompter:
def match_prompt_style(self):
if self.prompt_style == PromptStyle.instruct.value:
self.prompt_input = self.system_prompt + "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
self.prompt_no_input = self.system_no_input_prompt + "### Instruction:\n{instruction}\n\n### Response:\n"
self.prompt_input = (
self.system_prompt
+ "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
)
self.prompt_no_input = (
self.system_no_input_prompt
+ "### Instruction:\n{instruction}\n\n### Response:\n"
)
self.agent_label = "### Thought:\n{output}\n\n### Agent Reflection:\n{reflection}\n\n### Final Response:\n{corrected}"
self.response_split = "### Final Response:"
if self.prompt_style == PromptStyle.chat.value:
self.prompt_input = self.system_prompt + "USER: {instruction}\n{input}\nASSISTANT:"
self.prompt_no_input = self.system_no_input_prompt + "USER: {instruction}\nASSISTANT:"
self.agent_label = "\nTHOUGHT: {output}\nASSISTANT REFLECTION: {reflection}\nASSISTANT:"
self.prompt_input = (
self.system_prompt + "USER: {instruction}\n{input}\nASSISTANT:"
)
self.prompt_no_input = (
self.system_no_input_prompt + "USER: {instruction}\nASSISTANT:"
)
self.agent_label = (
"\nTHOUGHT: {output}\nASSISTANT REFLECTION: {reflection}\nASSISTANT:"
)
self.response_split = "ASSISTANT:"
def build_prompt(
@@ -167,7 +198,7 @@ class Conversation:
yield (role + ":", " " + message)
else:
logging.warning("role with empty message: " + role)
yield (role + ":", )
yield (role + ":",)
def copy(self):
return Conversation(
@@ -199,7 +230,9 @@ conv_vicuna_v1_1 = Conversation(
class ShareGPTPrompter:
def __init__(self, prompt_style=None):
if prompt_style != PromptStyle.chat.value:
raise Exception(f"unsupported prompt_style for ShareGPTPrompter({prompt_style})")
raise Exception(
f"unsupported prompt_style for ShareGPTPrompter({prompt_style})"
)
# def match_prompt_style(self):
# if self.prompt_style == PromptStyle.chat.value: