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import gradio as gr | |
import torch | |
from transformers import AutoModelForCausalLM, AutoTokenizer | |
# Model ID | |
model_id = "apu20/Llama-3.2-3B-Instruct_Tele" | |
# Load quantized model (switch to 8-bit if needed) | |
model = AutoModelForCausalLM.from_pretrained( | |
model_id, | |
torch_dtype=torch.float16, # Use float16 for reduced memory footprint | |
device_map="cpu" # Force model to run on CPU | |
) | |
# Load tokenizer | |
tokenizer = AutoTokenizer.from_pretrained(model_id) | |
def respond(message, history, system_message, max_tokens, temperature, top_p): | |
messages = [{"role": "system", "content": system_message}] | |
for val in history: | |
if val[0]: | |
messages.append({"role": "user", "content": val[0]}) | |
if val[1]: | |
messages.append({"role": "assistant", "content": val[1]}) | |
messages.append({"role": "user", "content": message}) | |
# Tokenize input | |
inputs = tokenizer(message, return_tensors="pt").to("cpu") # Ensure inputs are on CPU | |
# Generate response | |
with torch.no_grad(): | |
outputs = model.generate( | |
**inputs, | |
max_length=max_tokens, | |
temperature=temperature, | |
top_p=top_p | |
) | |
response = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
return response | |
# Gradio Chat Interface | |
demo = gr.ChatInterface( | |
respond, | |
additional_inputs=[ | |
gr.Textbox(value="You are a friendly Chatbot.", label="System message"), | |
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), | |
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), | |
gr.Slider( | |
minimum=0.1, | |
maximum=1.0, | |
value=0.95, | |
step=0.05, | |
label="Top-p (nucleus sampling)", | |
), | |
], | |
) | |
if __name__ == "__main__": | |
demo.launch() | |