Spaces:
Runtime error
Runtime error
File size: 6,711 Bytes
58332ae 82b4c6c 58332ae 82b4c6c 58332ae 82b4c6c 58332ae |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 |
import os
import gradio as gr
from text_generation import Client
HF_TOKEN = os.environ.get("HF_TOKEN", None)
API_URL = " https://api-inference.huggingface.co/models/BigCode/octocoder"
theme = gr.themes.Monochrome(
primary_hue="indigo",
secondary_hue="blue",
neutral_hue="slate",
radius_size=gr.themes.sizes.radius_sm,
font=[
gr.themes.GoogleFont("Open Sans"),
"ui-sans-serif",
"system-ui",
"sans-serif",
],
)
client = Client(
API_URL,
headers={"Authorization": f"Bearer {HF_TOKEN}"},
)
def generate(query:str, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0, ):
if query.endswith("."):
prompt = f"Question: {query}\n\nAnswer:"
else:
prompt = f"Question: {query}.\n\nAnswer:"
temperature = float(temperature)
if temperature < 1e-2:
temperature = 1e-2
top_p = float(top_p)
generate_kwargs = dict(
temperature=temperature,
max_new_tokens=max_new_tokens,
top_p=top_p,
repetition_penalty=repetition_penalty,
do_sample=True,
seed=42,
)
stream = client.generate_stream(prompt, **generate_kwargs)
output = ""
previous_token = ""
for response in stream:
if response.token.text == "<|endoftext|>":
return output
else:
output += response.token.text
previous_token = response.token.text
yield output
return output
def process_example(**krwags):
for x in generate(**krwags):
pass
return x
css = ".generating {visibility: hidden}"
monospace_css = """
#q-input textarea {
font-family: monospace, 'Consolas', Courier, monospace;
}
"""
description = """
<div style="text-align: center;">
<center><img src='https://raw.githubusercontent.com/bigcode-project/octopack/31f3320f098703c7910e43492c39366eeea68d83/banner.png' width='70%'/></center>
<br>
<h1><u> OctoCoder Demo </u></h1>
</div>
<br>
<div style="text-align: center;">
<p>This is a demo to demonstrate the capabilities of <a href="https://huggingface.co/bigcode/octocoder">OctoCoder</a> model by showing how it can be used to generate code by following the instructions provided in the input.</p>
<p><strong>OctoCoder</strong> is an instruction tuned model with 15.5B parameters created by finetuning StarCoder on CommitPackFT & OASST</p>
</div>
"""
disclaimer = """⚠️<b>Any use or sharing of this demo constitues your acceptance of the BigCode [OpenRAIL-M](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) License Agreement and the use restrictions included within.</b>\
<br>**Intended Use**: this app and its [supporting model](https://huggingface.co/bigcode) are provided for demonstration purposes; not to serve as replacement for human expertise. For more details on the model's limitations in terms of factuality and biases, see the [model card.](https://huggingface.co/bigcode)"""
examples = [
['Please write a function in Python that performs bubble sort.', 256],
['''Explain the following piece of code
def count_unique(s):
s = s.lower()
s_split = list(s)
valid_chars = [char for char in s_split if char.isalpha() or char == " "]
valid_sentence = "".join(valid_chars)
uniques = set(valid_sentence.split(" "))
return len(uniques)''', 512],
['Write an efficient Python function that takes a given text and returns its Morse code equivalent without using any third party library', 512],
['Write a html and css code to render a clock', 8000],
]
with gr.Blocks(theme=theme, analytics_enabled=False, css=css) as demo:
with gr.Column():
gr.Markdown(description)
with gr.Row():
with gr.Column():
with gr.Accordion("Settings", open=True):
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=256,
minimum=0,
maximum=8192,
step=64,
interactive=True,
info="The maximum numbers of new tokens",
)
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.Row():
with gr.Column():
instruction = gr.Textbox(
placeholder="Enter your query here",
lines=5,
label="Input",
elem_id="q-input",
)
submit = gr.Button("Generate", variant="primary")
output = gr.Code(elem_id="q-output", lines=30, label="Output")
gr.Markdown(disclaimer)
gr.Examples(
examples=examples,
inputs=[instruction, max_new_tokens],
cache_examples=False,
fn=process_example,
outputs=[output],
)
submit.click(
generate,
inputs=[instruction, temperature, max_new_tokens, top_p, repetition_penalty],
outputs=[output],
)
demo.queue(concurrency_count=16).launch(debug=True)
|