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import os
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
from huggingface_hub import login
login(token=os.getenv('HF_TOKEN'))
# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("Zyphra/Zamba2-7B")
model = AutoModelForCausalLM.from_pretrained(
"Zyphra/Zamba2-7B",
device_map="auto", # Automatically handles device placement
torch_dtype=torch.bfloat16
)
def generate_response(input_text):
input_ids = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(
**input_ids,
max_new_tokens=500,
do_sample=True,
temperature=0.7,
top_k=50,
top_p=0.9,
repetition_penalty=1.2,
num_beams=5,
length_penalty=1.0,
num_return_sequences=1
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
# Create the Gradio interface
demo = gr.Interface(
fn=generate_response,
inputs=gr.inputs.Textbox(lines=5, placeholder="Enter your question here..."),
outputs="text",
title="Zamba2-7B Model",
description="Ask Zamba2 7B a question."
)
if __name__ == "__main__":
demo.launch()
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