File size: 1,710 Bytes
c9c4226 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 |
import re
from typing import Dict, List, Any
from unsloth import FastLanguageModel
class EndpointHandler():
def __init__(self, path=""):
# Preload all the elements you are going to need at inference.
# pseudo:
# self.model= load_model(path)
max_seq_length = 2048
dtype = None
load_in_4bit = True
self.model, self.tokenizer = FastLanguageModel.from_pretrained(
model_name=path, # YOUR MODEL YOU USED FOR TRAINING
max_seq_length=max_seq_length,
dtype=dtype,
load_in_4bit=load_in_4bit,
)
FastLanguageModel.for_inference(self.model) # Enable native 2x faster inference
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
data args:
inputs (:obj: `str` | `PIL.Image` | `np.array`)
kwargs
Return:
A :obj:`list` | `dict`: will be serialized and returned
"""
messages = data.pop("inputs", data)
# messages = [
# {"from": "human", "value": "What is a famous tall tower in Paris?"},
# ]
inputs = self.tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True, # Must add for generation
return_tensors="pt",
).to("cuda")
outputs = self.model.generate(input_ids=inputs, max_new_tokens=1000, use_cache=True)
content = self.tokenizer.batch_decode(outputs)
pattern = r'\[INST\].*?\[/INST\]'
content = re.sub(pattern, '', content, flags=re.DOTALL)
content = content.replace('<s>', '').replace('</s>', '').strip()
return content
|