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on
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Running
on
Zero
File size: 2,263 Bytes
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import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from .Model import Model
import os
class HF_model(Model):
def __init__(self, config, device="cuda:0"):
super().__init__(config)
self.max_output_tokens = int(config["params"]["max_output_tokens"])
api_pos = int(config["api_key_info"]["api_key_use"])
hf_token = config["api_key_info"]["api_keys"][api_pos]
if hf_token is None or len(hf_token) == 0:
hf_token = os.getenv("HF_TOKEN")
self.tokenizer = AutoTokenizer.from_pretrained(self.name, use_auth_token=hf_token, trust_remote_code=True)
self.model = AutoModelForCausalLM.from_pretrained(
self.name,
torch_dtype=torch.bfloat16,
device_map=device,
token=hf_token,
trust_remote_code=True
)
def query(self, msg, max_tokens=128000):
messages = self.messages
messages[1]["content"] = msg
text = self.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = self.tokenizer([text], return_tensors="pt").to(self.model.device)
generated_ids = self.model.generate(
model_inputs.input_ids,
max_new_tokens=self.max_output_tokens,
temperature=self.temperature
)
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]
return response
def get_prompt_length(self,msg):
messages = self.messages
messages[1]["content"] = msg
input_ids = self.tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(self.model.device)
return len(input_ids[0])
def cut_context(self, msg, max_length):
tokens = self.tokenizer.encode(msg, add_special_tokens=True)
truncated_tokens = tokens[:max_length]
truncated_text = self.tokenizer.decode(truncated_tokens, skip_special_tokens=True)
return truncated_text |