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import gradio as gr
from transformers import AutoModel, AutoTokenizer
import torch
# Load model with caching
tokenizer = AutoTokenizer.from_pretrained(
"Qwen/Qwen3-Embedding-8B",
trust_remote_code=True
)
model = AutoModel.from_pretrained(
"Qwen/Qwen3-Embedding-8B",
trust_remote_code=True,
device_map="auto"
).eval()
def get_embedding(text):
inputs = tokenizer(text, return_tensors="pt", truncation=True).to(model.device)
with torch.no_grad():
outputs = model(**inputs)
embedding = outputs.last_hidden_state.mean(dim=1).squeeze().tolist()
return {"text": text, "embedding_size": len(embedding)}
demo = gr.Interface(
fn=get_embedding,
inputs=gr.Textbox(label="Input text"),
outputs=gr.JSON(),
title="Qwen3 Embeddings"
)
demo.launch() |