Minimal example of 8-bit inference for LLMs via Hugging Face transformers + accelerate.

inference_hf_8bit.py

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

MAX_NEW_TOKENS = 128
model_name = 'facebook/opt-6.7b'

text = """Hello, I am a prompt. Who are you?"""
tokenizer = AutoTokenizer.from_pretrained(model_name)
input_ids = tokenizer(text, return_tensors="pt").input_ids

free_in_GB = int(torch.cuda.mem_get_info()[0]/1024**3)
max_memory = f'{free_in_GB-2}GB'

n_gpus = torch.cuda.device_count()
max_memory = {i: max_memory for i in range(n_gpus)}

model = AutoModelForCausalLM.from_pretrained(
  model_name,
  device_map='auto',
  load_in_8bit=True,
  max_memory=max_memory
)
generated_ids = model.generate(input_ids, max_length=MAX_NEW_TOKENS)
print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))