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from typing import List, Dict
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
class EndpointHandler:
def __init__(self, path: str):
# Load model and tokenizer
self.tokenizer = AutoTokenizer.from_pretrained(path)
self.model = AutoModelForCausalLM.from_pretrained(
path,
torch_dtype=torch.float32, # Use float32 for CPU
device_map="auto"
)
# Set up generation parameters
self.default_params = {
"max_length": 1000,
"temperature": 0.7,
"top_p": 0.7,
"top_k": 50,
"repetition_penalty": 1.0,
"do_sample": True,
"pad_token_id": self.tokenizer.pad_token_id,
"eos_token_id": self.tokenizer.eos_token_id
}
def __call__(self, data: Dict):
"""
Args:
data: Dictionary with "inputs" and optional "parameters"
Returns:
Generated text
"""
# Extract messages from input
messages = data.get("inputs", {}).get("messages", [])
if not messages:
return {"error": "No messages provided"}
# Format input text
input_text = ""
for msg in messages:
role = msg.get("role", "")
content = msg.get("content", "")
input_text += f"{role}: {content}\n"
# Get generation parameters
params = {**self.default_params}
if "parameters" in data:
params.update(data["parameters"])
# Tokenize input
inputs = self.tokenizer(
input_text,
return_tensors="pt",
padding=True,
truncation=True,
max_length=512
)
# Generate response
with torch.no_grad():
outputs = self.model.generate(
inputs["input_ids"],
attention_mask=inputs["attention_mask"],
**params
)
# Decode response
generated_text = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
return [{"generated_text": generated_text}]
def preprocess(self, request):
"""
Prepare request for inference
"""
if request.content_type != "application/json":
raise ValueError("Content type must be application/json")
data = request.json
return data
def postprocess(self, data):
"""
Post-process model output
"""
return data |