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"""
Surpported Models.
Supports:
- Open Source:LLaMA3, Qwen2.5, MiniCPM3, ChatGLM4
- Closed Source: ChatGPT, DeepSeek
"""
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoConfig, GenerationConfig
import torch
import openai
import os
from openai import OpenAI
# The inferencing code is taken from the official documentation
class BaseEngine:
def __init__(self, model_name_or_path: str):
self.name = None
self.tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)
self.temperature = 0.2
self.top_p = 0.9
self.max_tokens = 1024
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def get_chat_response(self, prompt):
raise NotImplementedError
def set_hyperparameter(self, temperature: float = 0.2, top_p: float = 0.9, max_tokens: int = 1024):
self.temperature = temperature
self.top_p = top_p
self.max_tokens = max_tokens
class LLaMA(BaseEngine):
def __init__(self, model_name_or_path: str):
super().__init__(model_name_or_path)
self.name = "LLaMA"
self.model_id = model_name_or_path
self.pipeline = pipeline(
"text-generation",
model=self.model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
self.terminators = [
self.pipeline.tokenizer.eos_token_id,
self.pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
def get_chat_response(self, prompt):
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt},
]
outputs = self.pipeline(
messages,
max_new_tokens=self.max_tokens,
eos_token_id=self.terminators,
do_sample=True,
temperature=self.temperature,
top_p=self.top_p,
)
return outputs[0]["generated_text"][-1]['content'].strip()
class Qwen(BaseEngine):
def __init__(self, model_name_or_path: str):
super().__init__(model_name_or_path)
self.name = "Qwen"
self.model_id = model_name_or_path
self.model = AutoModelForCausalLM.from_pretrained(
self.model_id,
torch_dtype="auto",
device_map="auto"
)
def get_chat_response(self, prompt):
messages = [
{"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = self.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = self.tokenizer([text], return_tensors="pt").to(self.device)
generated_ids = self.model.generate(
**model_inputs,
temperature=self.temperature,
top_p=self.top_p,
max_new_tokens=self.max_tokens
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
return response
class MiniCPM(BaseEngine):
def __init__(self, model_name_or_path: str):
super().__init__(model_name_or_path)
self.name = "MiniCPM"
self.model_id = model_name_or_path
self.model = AutoModelForCausalLM.from_pretrained(
self.model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True
)
def get_chat_response(self, prompt):
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
model_inputs = self.tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(self.device)
model_outputs = self.model.generate(
model_inputs,
temperature=self.temperature,
top_p=self.top_p,
max_new_tokens=self.max_tokens
)
output_token_ids = [
model_outputs[i][len(model_inputs[i]):] for i in range(len(model_inputs))
]
response = self.tokenizer.batch_decode(output_token_ids, skip_special_tokens=True)[0].strip()
return response
class ChatGLM(BaseEngine):
def __init__(self, model_name_or_path: str):
super().__init__(model_name_or_path)
self.name = "ChatGLM"
self.model_id = model_name_or_path
self.model = AutoModelForCausalLM.from_pretrained(
self.model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
low_cpu_mem_usage=True,
trust_remote_code=True
)
def get_chat_response(self, prompt):
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
model_inputs = self.tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True, add_generation_prompt=True, tokenize=True).to(self.device)
model_outputs = self.model.generate(
**model_inputs,
temperature=self.temperature,
top_p=self.top_p,
max_new_tokens=self.max_tokens
)
model_outputs = model_outputs[:, model_inputs['input_ids'].shape[1]:]
response = self.tokenizer.batch_decode(model_outputs, skip_special_tokens=True)[0].strip()
return response
class OneKE(BaseEngine):
def __init__(self, model_name_or_path: str):
super().__init__(model_name_or_path)
self.name = "OneKE"
self.model_id = model_name_or_path
config = AutoConfig.from_pretrained(self.model_id, trust_remote_code=True)
quantization_config=BitsAndBytesConfig(
load_in_4bit=True,
llm_int8_threshold=6.0,
llm_int8_has_fp16_weight=False,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
)
self.model = AutoModelForCausalLM.from_pretrained(
self.model_id,
config=config,
device_map="auto",
quantization_config=quantization_config,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
)
def get_chat_response(self, prompt):
system_prompt = '<<SYS>>\nYou are a helpful assistant. 你是一个乐于助人的助手。\n<</SYS>>\n\n'
sintruct = '[INST] ' + system_prompt + prompt + '[/INST]'
input_ids = self.tokenizer.encode(prompt, return_tensors='pt')
input_ids = self.tokenizer.encode(sintruct, return_tensors="pt").to(self.device)
input_length = input_ids.size(1)
generation_output = self.model.generate(input_ids=input_ids, generation_config=GenerationConfig(max_length=1024, max_new_tokens=512, return_dict_in_generate=True,pad_token_id=self.tokenizer.pad_token_id,eos_token_id=self.tokenizer.eos_token_id))
generation_output = generation_output.sequences[0]
generation_output = generation_output[input_length:]
response = self.tokenizer.decode(generation_output, skip_special_tokens=True)
return response
class ChatGPT(BaseEngine):
def __init__(self, model_name_or_path: str, api_key: str, base_url=openai.base_url):
self.name = "ChatGPT"
self.model = model_name_or_path
self.base_url = base_url
self.temperature = 0.2
self.top_p = 0.9
self.max_tokens = 4096 # Close source model
if api_key != "":
self.api_key = api_key
else:
self.api_key = os.environ["OPENAI_API_KEY"]
self.client = OpenAI(api_key=self.api_key, base_url=self.base_url)
def get_chat_response(self, input):
response = self.client.chat.completions.create(
model=self.model,
messages=[
{"role": "user", "content": input},
],
stream=False,
temperature=self.temperature,
max_tokens=self.max_tokens,
stop=None
)
return response.choices[0].message.content
class DeepSeek(BaseEngine):
def __init__(self, model_name_or_path: str, api_key: str, base_url="https://api.deepseek.com"):
self.name = "DeepSeek"
self.model = model_name_or_path
self.base_url = base_url
self.temperature = 0.2
self.top_p = 0.9
self.max_tokens = 4096 # Close source model
if api_key != "":
self.api_key = api_key
else:
self.api_key = os.environ["DEEPSEEK_API_KEY"]
self.client = OpenAI(api_key=self.api_key, base_url=self.base_url)
def get_chat_response(self, input):
response = self.client.chat.completions.create(
model=self.model,
messages=[
{"role": "user", "content": input},
],
stream=False,
temperature=self.temperature,
max_tokens=self.max_tokens,
stop=None
)
return response.choices[0].message.content
class LocalServer(BaseEngine):
def __init__(self, model_name_or_path: str, base_url="http://localhost:8000/v1"):
self.name = model_name_or_path.split('/')[-1]
self.model = model_name_or_path
self.base_url = base_url
self.temperature = 0.2
self.top_p = 0.9
self.max_tokens = 1024
self.api_key = "EMPTY_API_KEY"
self.client = OpenAI(api_key=self.api_key, base_url=self.base_url)
def get_chat_response(self, input):
try:
response = self.client.chat.completions.create(
model=self.model,
messages=[
{"role": "user", "content": input},
],
stream=False,
temperature=self.temperature,
max_tokens=self.max_tokens,
stop=None
)
return response.choices[0].message.content
except ConnectionError:
print("Error: Unable to connect to the server. Please check if the vllm service is running and the port is 8080.")
except Exception as e:
print(f"Error: {e}")
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