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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch
# Load the model and tokenizer
model_name = "meta-llama/Llama-3.2-1B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map="auto",
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
)
# Initialize the pipeline
generator = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
device_map="auto",
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
max_new_tokens=512,
)
def respond(message, history, system_message, max_tokens, temperature, top_p):
prompt = f"{system_message}\n"
for user_msg, assistant_msg in history:
prompt += f"User: {user_msg}\nAssistant: {assistant_msg}\n"
prompt += f"User: {message}\nAssistant:"
response = generator(
prompt,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
do_sample=True,
)[0]['generated_text']
assistant_response = response.replace(prompt, "").strip()
history.append((message, assistant_response))
return assistant_response, history
demo = gr.ChatInterface(
fn=respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=1024, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.01, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.01,
label="Top-p (nucleus sampling)",
),
],
title="Chat with LLaMA 2",
description="A chat interface using LLaMA 2 model locally via Transformers.",
)
if __name__ == "__main__":
demo.launch()
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