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import torch | |
from .BaseLLM import BaseLLM | |
from transformers import AutoModelForCausalLM, AutoTokenizer | |
from transformers.generation.utils import GenerationConfig | |
from peft import PeftModel | |
tokenizer_BaiChuan = None | |
model_BaiChuan = None | |
def initialize_BaiChuan2LORA(): | |
global model_BaiChuan, tokenizer_BaiChuan | |
if model_BaiChuan is None: | |
model_BaiChuan = AutoModelForCausalLM.from_pretrained( | |
"baichuan-inc/Baichuan2-13B-Chat", | |
device_map="auto", | |
torch_dtype=torch.bfloat16, | |
trust_remote_code=True, | |
) | |
model_BaiChuan = PeftModel.from_pretrained( | |
model_BaiChuan, | |
"silk-road/Chat-Haruhi-Fusion_Baichuan2_13B" | |
) | |
model_BaiChuan.generation_config = GenerationConfig.from_pretrained( | |
"baichuan-inc/Baichuan2-13B-Chat" | |
) | |
if tokenizer_BaiChuan is None: | |
tokenizer_BaiChuan = AutoTokenizer.from_pretrained( | |
"baichuan-inc/Baichuan2-13B-Chat", | |
use_fast=True, | |
trust_remote_code=True | |
) | |
return model_BaiChuan, tokenizer_BaiChuan | |
def BaiChuan_tokenizer(text): | |
return len(tokenizer_BaiChuan.encode(text)) | |
class BaiChuan2GPT(BaseLLM): | |
def __init__(self, model = "haruhi-fusion-baichuan"): | |
super(BaiChuan2GPT, self).__init__() | |
if model == "baichuan2-13b": | |
self.tokenizer = AutoTokenizer.from_pretrained( | |
"baichuan-inc/Baichuan2-13B-Chat", | |
use_fast=True, | |
trust_remote_code=True | |
), | |
self.model = AutoModelForCausalLM.from_pretrained( | |
"baichuan-inc/Baichuan2-13B-Chat", | |
device_map="auto", | |
torch_dtype=torch.bfloat16, | |
trust_remote_code=True, | |
) | |
self.model.generation_config = GenerationConfig.from_pretrained( | |
"baichuan-inc/Baichuan2-13B-Chat" | |
) | |
elif model == "haruhi-fusion-baichuan": | |
self.model, self.tokenizer = initialize_BaiChuan2LORA() | |
else: | |
raise Exception("Unknown BaiChuan Model! Currently supported: [BaiChuan2-13B, haruhi-fusion-baichuan]") | |
self.messages = [] | |
def initialize_message(self): | |
self.messages = [] | |
def ai_message(self, payload): | |
self.messages.append({"role": "assistant", "content": payload}) | |
def system_message(self, payload): | |
self.messages.append({"role": "system", "content": payload}) | |
def user_message(self, payload): | |
self.messages.append({"role": "user", "content": payload}) | |
def get_response(self): | |
with torch.no_grad(): | |
response = self.model.chat(self.tokenizer, self.messages) | |
return response | |
def print_prompt(self): | |
print(type(self.messages)) | |
print(self.messages) |