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from typing import Any, Dict
import torch
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] == 8 else torch.float16
class EndpointHandler:
def __init__(self, path=""):
tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
path,
return_dict=True,
device_map="auto",
load_in_8bit=True,
torch_dtype=dtype,
trust_remote_code=True,
)
self.generation_config = model.generation_config
self.generation_config.max_new_tokens = 1000
self.generation_config.temperature = 0.7 # Changed from 0 to 0.7
self.generation_config.num_return_sequences = 1
self.generation_config.pad_token_id = tokenizer.eos_token_id
self.generation_config.eos_token_id = tokenizer.eos_token_id
self.pipeline = transformers.pipeline(
"text-generation", model=model, tokenizer=tokenizer
)
def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
prompt = data.pop("inputs", data)
result = self.pipeline(
prompt,
max_length=1000, # Added this line to set max_length
temperature=0.7, # Added this line to set temperature
top_p=0.9, # Added this line to set top_p
num_return_sequences=1, # Added this line to set num_return_sequences
pad_token_id=self.generation_config.pad_token_id,
eos_token_id=self.generation_config.eos_token_id,
return_full_text=True # Added this line to return full text
)
return {"generated_text": result}
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