File size: 1,618 Bytes
f288596 8c3dade f288596 8c3dade f288596 8c3dade f288596 8c3dade f288596 8c3dade f288596 8c3dade f288596 | 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 | import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from typing import Dict, Any
class EndpointHandler:
def __init__(self, path=""):
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.float16,
bnb_4bit_quant_type="nf4",
)
self.tokenizer = AutoTokenizer.from_pretrained(path)
self.model = AutoModelForCausalLM.from_pretrained(
path,
quantization_config=quantization_config,
device_map="auto",
torch_dtype=torch.float16,
)
def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
inputs = data.get("inputs", "")
parameters = data.get("parameters", {})
if isinstance(inputs, list):
text = self.tokenizer.apply_chat_template(inputs, tokenize=False, add_generation_prompt=True)
else:
text = inputs
encoded = self.tokenizer(text, return_tensors="pt").to(self.model.device)
max_new_tokens = parameters.get("max_new_tokens", 512)
temperature = parameters.get("temperature", 0.3)
with torch.no_grad():
outputs = self.model.generate(
**encoded,
max_new_tokens=max_new_tokens,
temperature=max(temperature, 0.01),
do_sample=temperature > 0,
)
new_tokens = outputs[0][encoded["input_ids"].shape[1]:]
response = self.tokenizer.decode(new_tokens, skip_special_tokens=True)
return [{"generated_text": response}]
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