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"""Module containing the AlpacaQAPromptTokenizingStrategy class""" |
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from typing import Tuple |
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from axolotl.prompt_tokenizers import ( |
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AlpacaPromptTokenizingStrategy, |
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InstructionPromptTokenizingStrategy, |
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) |
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from axolotl.prompters import AlpacaPrompter, PromptStyle |
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def load(tokenizer, cfg): |
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return AlpacaPromptTokenizingStrategy( |
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AlpacaPrompter(PromptStyle.CHAT.value), |
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tokenizer, |
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cfg.train_on_inputs, |
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cfg.sequence_len, |
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) |
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class AlpacaConcisePrompter(AlpacaPrompter): |
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""" |
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Alpaca Prompter extending the system prompt to ask for concise answers |
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""" |
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system_prompt = "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that concisely and appropriately completes the request.\n\n" |
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system_no_input_prompt = "Below is an instruction that describes a task. Write a response that appropriately and concisely completes the request.\n\n" |
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class AlpacaQAPromptTokenizingStrategy(InstructionPromptTokenizingStrategy): |
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""" |
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Tokenizing strategy for AlpacaQA |
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""" |
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def parse_instruction_fields(self, prompt) -> Tuple[str, str, str]: |
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return ( |
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prompt["question"], |
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"", |
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prompt["answer"], |
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) |
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class CamelAIPromptTokenizingStrategy(InstructionPromptTokenizingStrategy): |
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""" |
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Tokenizing strategy for CamelAI datasets |
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""" |
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def parse_instruction_fields(self, prompt) -> Tuple[str, str, str]: |
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return ( |
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prompt["message_1"], |
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"", |
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prompt["message_2"], |
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) |
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def load_concise(tokenizer, cfg): |
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return AlpacaPromptTokenizingStrategy( |
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AlpacaConcisePrompter(PromptStyle.CHAT.value), |
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tokenizer, |
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cfg.train_on_inputs, |
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cfg.sequence_len, |
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) |
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def load_qa(tokenizer, cfg): |
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return AlpacaQAPromptTokenizingStrategy( |
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AlpacaPrompter(PromptStyle.CHAT.value), |
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tokenizer, |
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cfg.train_on_inputs, |
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cfg.sequence_len, |
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) |
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def load_camel_ai(tokenizer, cfg): |
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return CamelAIPromptTokenizingStrategy( |
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AlpacaPrompter(PromptStyle.CHAT.value), |
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tokenizer, |
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cfg.train_on_inputs, |
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cfg.sequence_len, |
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) |
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