Instructions to use BAAI/AquilaCode-multi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BAAI/AquilaCode-multi with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("BAAI/AquilaCode-multi", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| import dataclasses | |
| from enum import auto, Enum | |
| from typing import List, Tuple, Any | |
| class SeparatorStyle(Enum): | |
| """Different separator style.""" | |
| SINGLE = auto() | |
| TWO = auto() | |
| class Conversation: | |
| """A class that keeps all conversation history.""" | |
| system: str | |
| instruction: str | |
| roles: List[str] | |
| messages: List[List[str]] | |
| offset: int | |
| sep_style: SeparatorStyle = SeparatorStyle.SINGLE | |
| sep: str = "###" | |
| sep2: str = None | |
| skip_next: bool = False | |
| conv_id: Any = None | |
| def get_prompt(self): | |
| if self.sep_style == SeparatorStyle.SINGLE: | |
| ret = self.system + self.sep | |
| if self.instruction is not None and len(self.instruction) > 0: | |
| ret += self.roles[2] + ": " + self.instruction + self.sep | |
| for role, message in self.messages: | |
| if message: | |
| ret += role + ": " + message + self.sep | |
| else: | |
| ret += role + ":" | |
| return ret | |
| elif self.sep_style == SeparatorStyle.TWO: | |
| seps = [self.sep, self.sep2] | |
| ret = self.system + seps[0] | |
| if self.instruction is not None and len(self.instruction) > 0: | |
| ret += self.roles[2] + ": " + self.instruction + self.sep | |
| for i, (role, message) in enumerate(self.messages): | |
| if message: | |
| ret += role + ": " + message + seps[i % 2] | |
| else: | |
| ret += role + ":" | |
| return ret | |
| else: | |
| raise ValueError(f"Invalid style: {self.sep_style}") | |
| def append_message(self, role, message): | |
| self.messages.append([role, message]) | |
| def to_gradio_chatbot(self): | |
| ret = [] | |
| for i, (role, msg) in enumerate(self.messages[self.offset:]): | |
| if i % 2 == 0: | |
| ret.append([msg, None]) | |
| else: | |
| ret[-1][-1] = msg | |
| return ret | |
| def copy(self): | |
| return Conversation( | |
| system=self.system, | |
| instruction=self.instruction, | |
| roles=self.roles, | |
| messages=[[x, y] for x, y in self.messages], | |
| offset=self.offset, | |
| sep_style=self.sep_style, | |
| sep=self.sep, | |
| sep2=self.sep2, | |
| conv_id=self.conv_id) | |
| def dict(self): | |
| return { | |
| "system": self.system, | |
| "instruction": self.instruction, | |
| "roles": self.roles, | |
| "messages": self.messages, | |
| "offset": self.offset, | |
| "sep": self.sep, | |
| "sep2": self.sep2, | |
| "conv_id": self.conv_id, | |
| } | |
| conv_v1 = Conversation( | |
| system="A chat between a curious human and an artificial intelligence assistant. " | |
| "The assistant gives helpful, detailed, and polite answers to the human's questions.", | |
| instruction="", | |
| roles=("Human", "Assistant", "System"), | |
| messages=(), | |
| offset=0, | |
| sep_style=SeparatorStyle.SINGLE, | |
| sep="###", | |
| ) | |
| conv_v1_2 = Conversation( | |
| system="A chat between a curious human and an artificial intelligence assistant. " | |
| "The assistant gives helpful, detailed, and polite answers to the human's questions.", | |
| instruction="", | |
| roles=("Human", "Assistant", "System"), | |
| messages=(), | |
| offset=0, | |
| sep_style=SeparatorStyle.SINGLE, | |
| sep="###", | |
| ) | |
| conv_bair_v1 = Conversation( | |
| system="BEGINNING OF CONVERSATION:", | |
| instruction="", | |
| roles=("USER", "GPT", "System"), | |
| messages=(), | |
| offset=0, | |
| sep_style=SeparatorStyle.TWO, | |
| sep=" ", | |
| sep2="</s>", | |
| ) | |
| default_conversation = conv_v1_2 | |
| conv_templates = { | |
| "v1": conv_v1_2, | |
| "bair_v1": conv_bair_v1, | |
| } | |
| def covert_prompt_to_input_ids_with_history(text, history, tokenizer, max_token): | |
| conv = default_conversation.copy() | |
| conv.append_message(conv.roles[1], None) | |
| conv.append_message(conv.roles[0], text) | |
| example = tokenizer.encode_plus(f"{conv.get_prompt()}", None, max_length=None)['input_ids'] | |
| while(len(history) > 0 and (len(example) < max_token)): | |
| tmp = history.pop() | |
| if tmp[0] == 'ASSISTANT': | |
| conv.append_message(conv.roles[1], tmp[1]) | |
| else: | |
| conv.append_message(conv.roles[0], tmp[1]) | |
| example = tokenizer.encode_plus(f"{conv.get_prompt()}", None, max_length=None)['input_ids'] | |
| if len(example) >= max_token: | |
| conv.messages.pop() | |
| conv.messages = conv.messages[::-1] | |
| print('model in:', conv.get_prompt()) | |
| example = tokenizer.encode_plus(f"{conv.get_prompt()}", None, max_length=None)['input_ids'] | |
| example = example[1:-1] | |
| return example | |
| if __name__ == "__main__": | |
| print(default_conversation.get_prompt()) | |