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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load the DeepSeek-R1-Distill-Qwen-1.5B-uncensored model
model_id = "thirdeyeai/DeepSeek-R1-Distill-Qwen-1.5B-uncensored"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16, # Use float16 for efficiency
low_cpu_mem_usage=True,
device_map="auto" # Automatically use available devices
)
def generate_text(prompt, max_length=100, temperature=0.7, top_p=0.9):
"""Generate text based on prompt"""
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
# Generate
with torch.no_grad():
generation_output = model.generate(
input_ids=inputs.input_ids,
attention_mask=inputs.attention_mask,
max_length=len(inputs.input_ids[0]) + max_length,
temperature=temperature,
top_p=top_p,
do_sample=True,
)
# Decode and return only the generated part
generated_text = tokenizer.decode(generation_output[0], skip_special_tokens=True)
return generated_text
# Create Gradio interface
demo = gr.Interface(
fn=generate_text,
inputs=[
gr.Textbox(lines=5, placeholder="Enter your prompt here...", label="Prompt"),
gr.Slider(minimum=10, maximum=500, value=100, step=10, label="Max Length"),
gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(minimum=0.1, maximum=1.0, value=0.9, step=0.05, label="Top-p")
],
outputs=gr.Textbox(label="Generated Text"),
title="DeepSeek-R1-Distill-Qwen-1.5B Demo",
description="Enter a prompt to generate text with the DeepSeek-R1-Distill-Qwen-1.5B-uncensored model."
)
# Launch the app
demo.launch() |