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import gradio as gr
import torch
from gradio.themes.utils import sizes
from transformers import AutoModelForCausalLM, AutoTokenizer
import utils
from constants import END_OF_TEXT, MIN_TEMPERATURE
# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(
"BEE-spoke-data/smol_llama-101M-GQA-python",
use_fast=False,
)
tokenizer.pad_token_id = tokenizer.eos_token_id
tokenizer.pad_token = END_OF_TEXT
model = AutoModelForCausalLM.from_pretrained(
"BEE-spoke-data/smol_llama-101M-GQA-python",
device_map="auto",
)
model = torch.compile(model, mode="reduce-overhead")
# UI things
_styles = utils.get_file_as_string("styles.css")
# Loads ./README.md file & splits it into sections
readme_file_content = utils.get_file_as_string("README.md", path="./")
(
manifest,
description,
disclaimer,
base_model_info,
formats,
) = utils.get_sections(readme_file_content, "---", up_to=5)
theme = gr.themes.Soft(
primary_hue="yellow",
secondary_hue="orange",
neutral_hue="slate",
radius_size=sizes.radius_sm,
font=[
gr.themes.GoogleFont("IBM Plex Sans", [400, 600]),
"ui-sans-serif",
"system-ui",
"sans-serif",
],
text_size=sizes.text_lg,
)
def run_inference(
prompt, temperature, max_new_tokens, top_p, repetition_penalty
) -> str:
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
do_sample=True,
epsilon_cutoff=1e-3,
max_new_tokens=max_new_tokens,
min_new_tokens=2,
no_repeat_ngram_size=6,
renormalize_logits=True,
repetition_penalty=repetition_penalty,
temperature=max(temperature, MIN_TEMPERATURE),
top_p=top_p,
)
text = tokenizer.batch_decode(
outputs,
skip_special_tokens=True,
)[0]
return text
examples = [
[
'def greet(name: str) -> None:\n """\n Greets the user\n """\n print(f"Hello,',
0.2,
64,
0.9,
1.2,
],
[
'for i in range(5):\n """\n Loop through 0 to 4\n """\n print(i,',
0.2,
64,
0.9,
1.2,
],
['x = 10\n"""Check if x is greater than 5"""\nif x > 5:', 0.2, 64, 0.9, 1.2],
["def square(x: int) -> int:\n return", 0.2, 64, 0.9, 1.2],
['import math\n"""Math operations"""\nmath.', 0.2, 64, 0.9, 1.2],
[
'def is_even(n) -> bool:\n """\n Check if a number is even\n """\n if n % 2 == 0:',
0.2,
64,
0.9,
1.2,
],
[
'while True:\n """Infinite loop example"""\n print("Infinite loop,',
0.2,
64,
0.9,
1.2,
],
[
"def sum_list(lst: list[int]) -> int:\n total = 0\n for item in lst:",
0.2,
64,
0.9,
1.2,
],
[
'try:\n """\n Exception handling\n """\n x = int(input("Enter a number: "))\nexcept ValueError:',
0.2,
64,
0.9,
1.2,
],
[
'def divide(a: float, b: float) -> float:\n """\n Divide a by b\n """\n if b != 0:',
0.2,
64,
0.9,
1.2,
],
]
# Define the Gradio Blocks interface
with gr.Blocks(theme=theme, analytics_enabled=False, css=_styles) as demo:
with gr.Column():
gr.Markdown(description)
with gr.Row():
with gr.Column():
instruction = gr.Textbox(
value=examples[0][0],
placeholder="Enter your code here",
label="Code",
elem_id="q-input",
)
submit = gr.Button("Generate", variant="primary")
output = gr.Code(elem_id="q-output", language="python", lines=10)
with gr.Row():
with gr.Column():
with gr.Accordion("Advanced settings", open=False):
with gr.Row():
column_1, column_2 = gr.Column(), gr.Column()
with column_1:
temperature = gr.Slider(
label="Temperature",
value=0.2,
minimum=0.0,
maximum=1.0,
step=0.05,
interactive=True,
info="Higher values produce more diverse outputs",
)
max_new_tokens = gr.Slider(
label="Max new tokens",
value=64,
minimum=32,
maximum=512,
step=32,
interactive=True,
info="Number of tokens to generate",
)
with column_2:
top_p = gr.Slider(
label="Top-p (nucleus sampling)",
value=0.90,
minimum=0.0,
maximum=1,
step=0.05,
interactive=True,
info="Higher values sample more low-probability tokens",
)
repetition_penalty = gr.Slider(
label="Repetition penalty",
value=1.2,
minimum=1.0,
maximum=2.0,
step=0.05,
interactive=True,
info="Penalize repeated tokens",
)
with gr.Column():
version = gr.Dropdown(
[
"smol_llama-101M-GQA-python",
],
value="smol_llama-101M-GQA-python",
label="Version",
info="",
)
gr.Markdown(disclaimer)
gr.Examples(
examples=examples,
inputs=[
instruction,
temperature,
max_new_tokens,
top_p,
repetition_penalty,
version,
],
cache_examples=False,
fn=run_inference,
outputs=[output],
)
gr.Markdown(base_model_info)
gr.Markdown(formats)
submit.click(
run_inference,
inputs=[
instruction,
temperature,
max_new_tokens,
top_p,
repetition_penalty,
],
outputs=[output],
# preprocess=False,
# batch=False,
show_progress=True,
)
# .queue(max_size=10, api_open=False)
demo.launch(
debug=True,
show_api=False,
share=utils.is_google_colab(),
)
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