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from typing import Dict, Any, List
from transformers import pipeline
import torch
class EndpointHandler:
def __init__(self, path=""):
# load model and processor from path
self.pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-beta", torch_dtype=torch.bfloat16, device_map="auto")
def __call__(self, data: Dict[str, Any]) -> List[List[Dict[str, str]]]:
"""
Args:
data (:dict:):
The payload with the text prompt and generation parameters.
"""
# process input
inputs = data.pop("inputs", data)
parameters = data.pop("parameters", None)
if isinstance(inputs, list) and isinstance(inputs[0], list) or isinstance(inputs[0], dict):
if isinstance(inputs[0], dict):
inputs = [inputs]
messages = inputs
else:
if isinstance(inputs, str):
messages = [[
{
"role": "system",
"content": "You are a helpful AI assistant",
},
{"role": "user", "content": inputs},
]]
else:
messages = [[
{
"role": "system",
"content": "You are a helpful AI assistant",
},
{"role": "user", "content": input},
] for input in inputs]
prompts = []
for message in messages:
prompts += [self.pipe.tokenizer.apply_chat_template(message, tokenize=False, add_generation_prompt=True)]
# pass inputs with all kwargs in data
if parameters is not None:
# Make endpoint compatible with hf client library
exclude_list = ["stop", "watermark", "details", "decoder_input_details"]
parameters = {name: val for name, val in parameters.items() if name not in exclude_list}
outputs = self.pipe(
prompts,
**parameters)
else:
outputs = self.pipe(
prompts, max_new_tokens=32,
)
return [{"generated_text": outputs}] |