File size: 1,514 Bytes
cf062ee | 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 | from typing import Any, Dict
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftConfig, PeftModel
class EndpointHandler:
def __init__(self, path: str = ""):
cfg = PeftConfig.from_pretrained(path)
base = cfg.base_model_name_or_path
self.tokenizer = AutoTokenizer.from_pretrained(base)
if self.tokenizer.pad_token_id is None:
self.tokenizer.pad_token_id = self.tokenizer.eos_token_id
model = AutoModelForCausalLM.from_pretrained(
base,
torch_dtype=torch.float16,
device_map="auto",
)
self.model = PeftModel.from_pretrained(model, path)
self.model.eval()
def __call__(self, data: Dict[str, Any]):
inputs = data.get("inputs", "")
params = data.get("parameters", {}) or {}
enc = self.tokenizer(inputs, return_tensors="pt").to(self.model.device)
with torch.no_grad():
out = self.model.generate(
**enc,
max_new_tokens=int(params.get("max_new_tokens", 256)),
temperature=float(params.get("temperature", 0.7)),
top_p=float(params.get("top_p", 0.9)),
do_sample=bool(params.get("do_sample", True)),
pad_token_id=self.tokenizer.pad_token_id,
)
text = self.tokenizer.decode(
out[0][enc["input_ids"].shape[1]:], skip_special_tokens=True
)
return [{"generated_text": text}]
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