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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) |