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import gradio as gr
import spaces
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
# HTML template for custom UI
HTML_TEMPLATE = """
<style>
body { background: linear-gradient(135deg, #f5f7fa, #c3cfe2); }
#app-header {
text-align: center;
background: rgba(255, 255, 255, 0.8);
padding: 20px;
border-radius: 10px;
box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
position: relative;
}
#app-header h1 { color: #4CAF50; font-size: 2em; margin-bottom: 10px; }
.concept { position: relative; transition: transform 0.3s; }
.concept:hover { transform: scale(1.1); }
.concept img {
width: 100px;
border-radius: 10px;
box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
}
.concept-description {
position: absolute;
bottom: -30px;
left: 50%;
transform: translateX(-50%);
background-color: #4CAF50;
color: white;
padding: 5px 10px;
border-radius: 5px;
opacity: 0;
transition: opacity 0.3s;
}
.concept:hover .concept-description { opacity: 1; }
.artifact {
position: absolute;
background: rgba(76, 175, 80, 0.1);
border-radius: 50%;
}
.artifact.large { width: 300px; height: 300px; top: -50px; left: -150px; }
.artifact.medium { width: 200px; height: 200px; bottom: -50px; right: -100px; }
.artifact.small {
width: 100px;
height: 100px;
top: 50%;
left: 50%;
transform: translate(-50%, -50%);
}
</style>
<div id="app-header">
<div class="artifact large"></div>
<div class="artifact medium"></div>
<div class="artifact small"></div>
<h1>Llama-3.1-Storm-8B Text Generator</h1>
<p>Generate text using the Llama-3.1-Storm-8B model by providing a prompt.</p>
<div style="display: flex; justify-content: center; gap: 20px; margin-top: 20px;">
<div class="concept">
<img src="https://raw.githubusercontent.com/huggingface/huggingface.js/main/packages/inference/src/tasks/images/llama.png" alt="Llama">
<div class="concept-description">Llama Model</div>
</div>
<div class="concept">
<img src="https://raw.githubusercontent.com/huggingface/huggingface.js/main/packages/inference/src/tasks/images/language.png" alt="Language">
<div class="concept-description">Natural Language Processing</div>
</div>
<div class="concept">
<img src="https://raw.githubusercontent.com/huggingface/huggingface.js/main/packages/inference/src/tasks/images/text-generation.png" alt="Text Generation">
<div class="concept-description">Text Generation</div>
</div>
</div>
</div>
"""
# Load the model and tokenizer
model_name = "akjindal53244/Llama-3.1-Storm-8B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
device_map="auto"
)
@spaces.GPU(duration=120)
def generate_text(prompt, max_length, temperature):
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
formatted_prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
inputs = tokenizer(formatted_prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=max_length,
do_sample=True,
temperature=temperature,
top_k=100,
top_p=0.95,
)
return tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
# Create Gradio interface
iface = gr.Interface(
fn=generate_text,
inputs=[
gr.Textbox(lines=5, label="Prompt"),
gr.Slider(minimum=1, maximum=500, value=128, step=1, label="Max Length"),
gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature"),
],
outputs=gr.Textbox(lines=10, label="Generated Text"),
title="Llama-3.1-Storm-8B Text Generation",
description="Enter a prompt to generate text using the Llama-3.1-Storm-8B model.",
article=HTML_TEMPLATE
)
iface.launch() |