Spaces:
Running
Running
Nadav Eden
commited on
Commit
·
e425a6f
1
Parent(s):
7f774ed
first version, only llms are functional
Browse files- app.py +81 -52
- requirements.txt +4 -1
app.py
CHANGED
@@ -1,64 +1,93 @@
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import gradio as gr
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from
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"""
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def
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max_tokens,
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temperature,
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top_p,
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):
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messages = [{"role": "system", "content": system_message}]
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for message in client.chat_completion(
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messages,
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max_tokens=max_tokens,
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stream=True,
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temperature=temperature,
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top_p=top_p,
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):
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token = message.choices[0].delta.content
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response += token
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yield response
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"""
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For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
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"""
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demo = gr.ChatInterface(
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respond,
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additional_inputs=[
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gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
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gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
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gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
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gr.Slider(
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minimum=0.1,
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maximum=1.0,
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value=0.95,
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step=0.05,
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label="Top-p (nucleus sampling)",
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),
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],
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)
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if __name__ == "__main__":
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demo.launch()
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#!/usr/bin/env python3
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import gradio as gr
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from transformers import AutoTokenizer, AutoModelForCausalLM
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llms = {
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"Qwen2-1.5B": {"model": "Qwen/Qwen2-1.5B-Instruct", "prefix": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
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"Qwen2-3B": {"model": "Qwen/Qwen2-3B-Instruct", "prefix": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
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"Qwen2-7B": {"model": "Qwen/Qwen2-7B-Instruct", "prefix": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
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"Qwen2.5-1.5B": {"model": "Qwen/Qwen2.5-1.5B-Instruct", "prefix": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
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"Qwen2.5-3B": {"model": "Qwen/Qwen2.5-3B-Instruct", "prefix": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
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"DeepSeek-Coder": {"model": "DeepSeek/DeepSeek-Coder", "prefix": "You are a helpful assistant."},
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}
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vlms = dict()
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def run_example(text_input, model_id="Qwen2-1.5B"):
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global messages
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tokenizer = AutoTokenizer.from_pretrained(llms[model_id]["model"], trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(llms[model_id]["model"], trust_remote_code=True)
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system_prompt = llms[model_id]["prefix"]
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if messages is None:
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": text_input},
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]
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else:
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messages.append({"role": "user", "content": text_input})
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=512,
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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return response
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messages = list()
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def reset_conversation():
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global messages
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messages = list()
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with gr.Blocks() as demo:
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gr.Markdown(
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"""
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# LLM & VLM Demo
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Use the different LLMs or VLMs to experience the different models.
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""")
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with gr.Tab(label="LLM"):
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with gr.Row():
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with gr.Column():
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model_selector = gr.Dropdown(choices=list(llms.keys()), label="Model", value="Qwen2-1.5B")
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text_input = gr.Textbox(label="User Prompt")
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submit_btn = gr.Button(value="Submit")
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reset_btn = gr.Button(value="Reset conversation")
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with gr.Column():
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model_output_text = gr.Textbox(label="Model Output Text")
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submit_btn.click(run_example,
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[text_input, model_selector],
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[model_output_text])
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reset_btn.click(reset_conversation)
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with gr.Tab(label="VLM (WIP)"):
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with gr.Row():
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with gr.Column():
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input_img = gr.Image(label="Input Image", type="pil")
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model_selector = gr.Dropdown(choices=list(vlms.keys()), label="Model", value="Qwen2-1.5B")
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text_input = gr.Textbox(label="User Prompt")
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submit_btn = gr.Button(value="Submit")
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reset_btn = gr.Button(value="Reset conversation")
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if __name__ == "__main__":
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demo.launch()
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requirements.txt
CHANGED
@@ -1 +1,4 @@
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huggingface_hub==0.25.2
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huggingface_hub==0.25.2
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torch
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transformers
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gradio
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