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