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import gradio as gr
import os
import torch
import subprocess
from transformers import AutoModelForCausalLM
from huggingface_hub import login
# Install required package
subprocess.run(
"pip install flash-attn --no-build-isolation",
env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
shell=True,
)
hf_token = os.getenv("HF_TOKEN")
login(token=hf_token, add_to_git_credential=True)
# Function to get the model summary
@spaces.GPU
def get_model_summary(model_name):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True).to(device)
return str(model)
# Create the Gradio Blocks interface
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
textbox = gr.Textbox(label="Model Name")
examples = gr.Examples(
examples=[
["google/gemma-7b"],
["microsoft/Phi-3-mini-4k-instruct"],
["meta-llama/Meta-Llama-3-8B"],
["mistralai/Mistral-7B-Instruct-v0.3"],
["vikhyatk/moondream2"],
["microsoft/Phi-3-vision-128k-instruct"],
["openbmb/MiniCPM-Llama3-V-2_5"],
["google/paligemma-3b-mix-224"],
["HuggingFaceM4/idefics2-8b-chatty"],
["mistralai/Codestral-22B-v0.1"]
],
inputs=textbox
)
submit_button = gr.Button("Submit")
with gr.Column():
output = gr.Textbox(label="Output", lines=20)
submit_button.click(fn=get_model_summary, inputs=textbox, outputs=output)
# Launch the interface
demo.launch()
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