File size: 1,301 Bytes
1041b24 b2118f5 1041b24 65aeece b2118f5 1041b24 5d16554 b2118f5 5d16554 1041b24 5d16554 ed13d86 5d16554 1041b24 5d16554 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 |
import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("TuringsSolutions/Gemma2LegalEdition", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("TuringsSolutions/Gemma2LegalEdition", trust_remote_code=True)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
def predict(prompt, temperature, max_tokens):
inputs = tokenizer(prompt, return_tensors="pt").to(device)
outputs = model.generate(
**inputs,
max_new_tokens=max_tokens,
temperature=temperature
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
# Create Gradio interface
iface = gr.Interface(
fn=predict,
inputs=[
gr.Textbox(lines=2, placeholder="Enter your prompt here..."),
gr.Slider(minimum=0.1, maximum=1.0, value=0.7, label="Temperature"),
gr.Slider(minimum=10, maximum=200, value=50, step=10, label="Number of Output Tokens")
],
outputs="text",
title="Phi3 Law Case Management Model",
description="A model to assist with law case management. Adjust the temperature and number of output tokens as needed."
)
# Launch the Gradio app
iface.launch() |