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import gradio as gr
from test_prompt_generator import generate_prompt
tokenizers = [
"google/gemma-7b",
"mistralai/Mistral-7B-v0.1",
"facebook/opt-2.7b",
"microsoft/phi-2",
"THUDM/chatglm3-6b",
"Qwen/Qwen1.5-7B-Chat",
"bigscience/bloom-560m",
"ise-uiuc/Magicoder-S-DS-6.7B",
"google/flan-t5-base",
"TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"google-bert/bert-base-uncased",
]
def generate(model_id, num_tokens, prefix=None, source_text=None):
output_file = f"prompt_{num_tokens}.jsonl"
prompt = generate_prompt(model_id, int(num_tokens), prefix=prefix, source_text=source_text, output_file=output_file)
return prompt, output_file
demo = gr.Interface(
fn=generate,
title="Test Prompt Generator",
description="Generate prompts with a given number of tokens for testing transformer models. "
"Prompt source: https://archive.org/stream/alicesadventures19033gut/19033.txt",
inputs=[
gr.Dropdown(
label="Tokenizer",
choices=tokenizers,
value="mistralai/Mistral-7B-v0.1",
allow_custom_value=True,
info="Select a tokenizer from this list or paste a model_id from a model on the Hugging Face Hub",
),
gr.Number(
label="Number of Tokens", minimum=4, maximum=2048, value=32, info="Enter a number between 4 and 2048."
),
gr.Textbox(
label="Prefix (optional)",
info="If given, the start of the prompt will be this prefix. Example: 'Summarize the following text:'",
),
gr.Textbox(
label="Source text (optional)",
info="By default, prompts will be generated from Alice in Wonderland. Enter text here to use that instead.",
),
],
outputs=[gr.Textbox(label="prompt", show_copy_button=True), gr.File(label="Json file")],
examples=[
["mistralai/Mistral-7B-v0.1", 32],
["mistralai/Mistral-7B-v0.1", 64],
["mistralai/Mistral-7B-v0.1", 128],
["mistralai/Mistral-7B-v0.1", 512],
["mistralai/Mistral-7B-v0.1", 1024],
["mistralai/Mistral-7B-v0.1", 2048],
],
cache_examples=False,
allow_flagging=False,
)
demo.launch()
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