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import gradio as gr
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load the model and config when the script starts
config = PeftConfig.from_pretrained("phearion/bigbrain-v0.0.1")
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
model = PeftModel.from_pretrained(model, "phearion/bigbrain-v0.0.1")
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
def greet(text):
batch = tokenizer(f"'{text}' ->: ", return_tensors='pt')
# Use torch.no_grad to disable gradient calculation
with torch.cuda.amp.autocast():
output_tokens = model.generate(**batch, max_new_tokens=20)
return tokenizer.decode(output_tokens[0], skip_special_tokens=True)
iface = gr.Interface(fn=greet, inputs="text", outputs="text")
iface.launch() |