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import os
import torch
import transformers
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
class ChatService:
def __init__(self):
pass
@staticmethod
def load_model(model_name=""):
global tokenizer, pipeline
print("Loading " + model_name + "...")
# config
gpu_count = torch.cuda.device_count()
print('gpu_count', gpu_count)
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
pipeline = transformers.pipeline(
task="text-generation",
model=model_name,
torch_dtype=torch.float16,
device_map="auto",
)
@staticmethod
def generate_message(req):
history = req["chat"]
assistant_name = req["assistant_name"] + ": "
system_message = req.get("system_message") if req.get("system_message") is not None else ""
temperature = req.get("temperature") if req.get("temperature") is not None else 1
top_p = req.get("top_p") if req.get("top_p") is not None else 1
top_k = req.get("top_k") if req.get("top_k") is not None else 10
max_length = req.get("max_length") if req.get("max_length") is not None else 1000
ending_tag = "[/INST]"
fulltext = "[INST] <<SYS>>" + system_message + "<</SYS>>" + "\n\n".join(
history) + "\n\n" + assistant_name + ending_tag
sequences = pipeline(
fulltext,
do_sample=True,
temperature=temperature,
top_k=top_k,
top_p=top_p,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
max_length=max_length,
)
response = sequences[0]['generated_text'].split(ending_tag)[1].split(assistant_name)
response = response[1] if len(response) > 1 else response[0]
response = response.strip()
return response
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