Spaces:
Sleeping
Sleeping
File size: 1,447 Bytes
a9408de f65b360 741290e a9408de f65b360 a9408de f65b360 a9408de f65b360 a9408de f65b360 741290e f65b360 a9408de |
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 |
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import gradio as gr
model_id = "distilbert/distilgpt2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float32)
model.eval()
def generate_text(prompt, temperature, top_k, top_p, max_tokens, repetition_penalty):
inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
output = model.generate(
**inputs,
max_new_tokens=max_tokens,
do_sample=True,
temperature=temperature,
top_k=top_k,
top_p=top_p,
repetition_penalty=repetition_penalty,
pad_token_id=tokenizer.eos_token_id
)
return tokenizer.decode(output[0], skip_special_tokens=True)
gr.Interface(
fn=generate_text,
inputs=[
gr.Textbox(label="Prompt", placeholder="Type something here...", lines=4),
gr.Slider(0.1, 1.5, value=1.0, step=0.1, label="Temperature"),
gr.Slider(1, 100, value=50, step=1, label="Top-K"),
gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top-P"),
gr.Slider(10, 512, value=128, step=1, label="Max New Tokens"),
gr.Slider(0.5, 2.0, value=1.0, step=0.1, label="Repetition Penalty")
],
outputs=gr.Textbox(label="Generated Text"),
title="🧠 AlphaMindQ Fork distilbert/distilgpt2",
theme="default"
).launch()
|