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import gradio as gr
# Load your model and tokenizer
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Specify the model name
model_name = "ahmedbasemdev/llama-3.2-3b-ChatBot"
# Load the model with 8-bit quantization
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map="auto", # Automatically map the model to the available device (CPU)
load_in_8bit=True, # Enable 8-bit quantization
torch_dtype=torch.float16 # Use mixed precision
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
def single_inference(question):
messages = []
messages.append({"role": "user", "content": question})
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(
input_ids,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.2,
)
response = outputs[0][input_ids.shape[-1]:]
output = tokenizer.decode(response, skip_special_tokens=True)
return output
# Create the Gradio interface
interface = gr.Interface(
fn=single_inference, # Function to wrap
inputs=gr.Textbox(lines=2, placeholder="Ask a question..."), # Input type
outputs=gr.Textbox(label="Response"), # Output type
title="Chat with Your Model", # App title
description="Enter a question, and the model will generate a response.", # App description
)
# Launch the app
if __name__ == "__main__":
interface.launch()
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