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from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from peft import PeftModel, PeftConfig
import gc
gc.collect()
model_name = "MoodChartAI/basicmood"
adapters_name = ""
torch.cuda.empty_cache()
os.system("sudo swapoff -a; swapon -a")
print(f"Starting to load the model {model_name} into memory")
m = AutoModelForCausalLM.from_pretrained(
model_name,
#load_in_4bit=True,
).to(device='cpu:7')
print(f"Loading the adapters from {adapters_name}")
m = PeftModel.from_pretrained(m, adapters_name)
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-1.3B", trust_remote_code=True)
while True:
mood_input = input("Mood: ")
inputs = tokenizer("Prompt: %s Completions: You're feeling"%mood_input, return_tensors="pt", return_attention_mask=True)
inputs.to(device='cpu:8')
outputs = m.generate(**inputs, max_length=12)
print(tokenizer.batch_decode(outputs)[0])
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