File size: 3,673 Bytes
3553172 8cc0aad 3553172 37293dc ce34d64 612aabd 4ea9a66 3553172 4ea9a66 3553172 ce34d64 4ea9a66 3a50377 4ac9e25 4b43a66 4ac9e25 4b43a66 4ac9e25 7925ddc 8d20e0a 7925ddc 3a50377 8cc0aad 3a50377 4ac9e25 59bb219 4ac9e25 3a50377 4b43a66 ce34d64 3a50377 4ac9e25 4b43a66 4ac9e25 612aabd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 |
"""Module for Alpaca prompt strategy classes"""
from typing import Any, Dict, Optional, Tuple
from axolotl.prompt_tokenizers import (
AlpacaPromptTokenizingStrategy,
InstructionPromptTokenizingStrategy,
)
from axolotl.prompters import AlpacaPrompter, PromptStyle, UnpromptedPrompter
def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
prompt_style = PromptStyle.CHAT.value
if ds_cfg and "conversation" in ds_cfg:
prompt_style = ds_cfg["conversation"]
return AlpacaPromptTokenizingStrategy(
AlpacaPrompter(prompt_style),
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,
)
|