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"""Module containing the AlpacaQAPromptTokenizingStrategy class"""
from typing import Tuple
from axolotl.prompt_tokenizers import (
AlpacaPromptTokenizingStrategy,
InstructionPromptTokenizingStrategy,
)
from axolotl.prompters import AlpacaPrompter, PromptStyle, UnpromptedPrompter
def load(tokenizer, cfg):
return AlpacaPromptTokenizingStrategy(
AlpacaPrompter(PromptStyle.CHAT.value),
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
class AlpacaConcisePrompter(AlpacaPrompter):
"""
Alpaca Prompter extending the system prompt to ask for concise chat-instruct answers
"""
system_prompt = "Below is an instruction from a USER that describes a task, paired with an input that provides further context. The ASSISTANT writes a response that concisely and appropriately completes the request.\n\n"
system_no_input_prompt = "Below is an instruction from a USER that describes a task. The ASSISTANT writes a response that appropriately and concisely completes the request.\n\n"
class AlpacaChatPrompter(AlpacaPrompter):
"""
Alpaca Chat Prompter extending the system prompt to for chat-instruct answers
"""
system_prompt = "Below is an instruction from a USER that describes a task, paired with an input that provides further context. The ASSISTANT writes a response that concisely and appropriately completes the request.\n\n"
system_no_input_prompt = "Below is an instruction from a USER that describes a task. The ASSISTANT writes a response that appropriately and concisely completes the request.\n\n"
def __init__(self): # pylint: disable=super-init-not-called
self.prompt_style = PromptStyle.CHAT.value
self.match_prompt_style()
class NoSystemPrompter(AlpacaPrompter):
"""
Null Prompter with no system prompts
"""
system_prompt = ""
system_no_input_prompt = ""
turn_format = "{instruction} {input} "
turn_no_input_format = "{instruction} "
def __init__(self): # pylint: disable=super-init-not-called
pass
class AlpacaQAPromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
"""
Tokenizing strategy for AlpacaQA
"""
def parse_instruction_fields(self, prompt) -> Tuple[str, str, str]:
return (
prompt["question"],
"",
prompt["answer"],
)
class CamelAIPromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
"""
Tokenizing strategy for CamelAI datasets
"""
def parse_instruction_fields(self, prompt) -> Tuple[str, str, str]:
return (
prompt["message_1"],
"",
prompt["message_2"],
)
def load_concise(tokenizer, cfg):
return AlpacaPromptTokenizingStrategy(
AlpacaConcisePrompter(PromptStyle.CHAT.value),
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
def load_qa(tokenizer, cfg):
return AlpacaQAPromptTokenizingStrategy(
AlpacaChatPrompter(),
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
def load_camel_ai(tokenizer, cfg):
return CamelAIPromptTokenizingStrategy(
AlpacaChatPrompter(),
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
def load_no_prompt(tokenizer, cfg):
return AlpacaPromptTokenizingStrategy(
UnpromptedPrompter(PromptStyle.CHAT.value),
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
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