File size: 2,101 Bytes
9c317f9 7eeefc1 9c317f9 7eeefc1 9c317f9 7eeefc1 9c317f9 7eeefc1 9c317f9 7eeefc1 9c317f9 7eeefc1 9c317f9 7eeefc1 9c317f9 |
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 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 |
import os
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
from huggingface_hub import login
# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("Zyphra/Zamba2-7B")
model = AutoModelForCausalLM.from_pretrained(
"Zyphra/Zamba2-7B",
device_map="cuda", # Automatically handles device placement
torch_dtype=torch.bfloat16
)
# Define the function to generate responses
def generate_response(input_text, max_new_tokens, temperature, top_k, top_p, repetition_penalty, num_beams, length_penalty):
# Tokenize and move input to model's device
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(model.device)
# Generate response using specified parameters
outputs = model.generate(
input_ids=input_ids,
max_new_tokens=max_new_tokens,
do_sample=True,
temperature=temperature,
top_k=top_k,
top_p=top_p,
repetition_penalty=repetition_penalty,
num_beams=num_beams,
length_penalty=length_penalty,
num_return_sequences=1
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
# Create Gradio interface with adjustable parameters
demo = gr.Interface(
fn=generate_response,
inputs=[
gr.Textbox(lines=1, placeholder="Enter a text to prepend...", label="Input Text"),
gr.Slider(50, 1000, step=50, value=500, label="Max New Tokens"),
gr.Slider(0.1, 1.5, step=0.1, value=0.7, label="Temperature"),
gr.Slider(1, 100, step=1, value=50, label="Top K"),
gr.Slider(0.1, 1.0, step=0.1, value=0.9, label="Top P"),
gr.Slider(1.0, 2.0, step=0.1, value=1.2, label="Repetition Penalty"),
gr.Slider(1, 10, step=1, value=5, label="Number of Beams"),
gr.Slider(0.0, 2.0, step=0.1, value=1.0, label="Length Penalty")
],
outputs=gr.Textbox(label="Generated Response"),
title="Zamba2-7B Model",
description="Ask Zamba2 7B a question with customizable parameters."
)
if __name__ == "__main__":
demo.launch()
|