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import json | |
import os | |
import shutil | |
import requests | |
import gradio as gr | |
from huggingface_hub import Repository | |
from text_generation import Client | |
from share_btn import community_icon_html, loading_icon_html, share_js, share_btn_css | |
HF_TOKEN = os.environ.get("HF_TOKEN", None) | |
API_URL = "https://api-inference.huggingface.co/models/bigcode/starcoder" | |
API_URL_BASE ="https://api-inference.huggingface.co/models/bigcode/starcoderbase" | |
API_URL_PLUS = "https://api-inference.huggingface.co/models/bigcode/starcoderplus" | |
FIM_PREFIX = "<fim_prefix>" | |
FIM_MIDDLE = "<fim_middle>" | |
FIM_SUFFIX = "<fim_suffix>" | |
FIM_INDICATOR = "<FILL_HERE>" | |
FORMATS = """## Model Formats | |
The model is pretrained on code and is formatted with special tokens in addition to the pure code data,\ | |
such as prefixes specifying the source of the file or tokens separating code from a commit message.\ | |
Use these templates to explore the model's capacities: | |
### 1. Prefixes 🏷️ | |
For pure code files, use any combination of the following prefixes: | |
``` | |
<reponame>REPONAME<filename>FILENAME<gh_stars>STARS\ncode<|endoftext|> | |
``` | |
STARS can be one of: 0, 1-10, 10-100, 100-1000, 1000+ | |
### 2. Commits 💾 | |
The commits data is formatted as follows: | |
``` | |
<commit_before>code<commit_msg>text<commit_after>code<|endoftext|> | |
``` | |
### 3. Jupyter Notebooks 📓 | |
The model is trained on Jupyter notebooks as Python scripts and structured formats like: | |
``` | |
<start_jupyter><jupyter_text>text<jupyter_code>code<jupyter_output>output<jupyter_text> | |
``` | |
### 4. Issues 🐛 | |
We also trained on GitHub issues using the following formatting: | |
``` | |
<issue_start><issue_comment>text<issue_comment>...<issue_closed> | |
``` | |
### 5. Fill-in-the-middle 🧩 | |
Fill in the middle requires rearranging the model inputs. The playground handles this for you - all you need is to specify where to fill: | |
``` | |
code before<FILL_HERE>code after | |
``` | |
""" | |
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}"}, | |
) | |
client_base = Client( | |
API_URL_BASE, headers={"Authorization": f"Bearer {HF_TOKEN}"}, | |
) | |
client_plus = Client( | |
API_URL_PLUS, headers={"Authorization": f"Bearer {HF_TOKEN}"}, | |
) | |
def generate( | |
prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0, version="StarCoder", | |
): | |
temperature = float(temperature) | |
if temperature < 1e-2: | |
temperature = 1e-2 | |
top_p = float(top_p) | |
fim_mode = False | |
generate_kwargs = dict( | |
temperature=temperature, | |
max_new_tokens=max_new_tokens, | |
top_p=top_p, | |
repetition_penalty=repetition_penalty, | |
do_sample=True, | |
seed=42, | |
) | |
if FIM_INDICATOR in prompt: | |
fim_mode = True | |
try: | |
prefix, suffix = prompt.split(FIM_INDICATOR) | |
except: | |
raise ValueError(f"Only one {FIM_INDICATOR} allowed in prompt!") | |
prompt = f"{FIM_PREFIX}{prefix}{FIM_SUFFIX}{suffix}{FIM_MIDDLE}" | |
if version == "StarCoder": | |
stream = client.generate_stream(prompt, **generate_kwargs) | |
elif version == "StarCoderPlus": | |
stream = client_plus.generate_stream(prompt, **generate_kwargs) | |
else: | |
stream = client_base.generate_stream(prompt, **generate_kwargs) | |
if fim_mode: | |
output = prefix | |
else: | |
output = prompt | |
previous_token = "" | |
for response in stream: | |
if response.token.text == "<|endoftext|>": | |
if fim_mode: | |
output += suffix | |
else: | |
return output | |
else: | |
output += response.token.text | |
previous_token = response.token.text | |
yield output | |
return output | |
examples = [ | |
"X_train, y_train, X_test, y_test = train_test_split(X, y, test_size=0.1)\n\n# Train a logistic regression model, predict the labels on the test set and compute the accuracy score", | |
"// Returns every other value in the array as a new array.\nfunction everyOther(arr) {", | |
"Poor English: She no went to the market. Corrected English:", | |
"def alternating(list1, list2):\n results = []\n for i in range(min(len(list1), len(list2))):\n results.append(list1[i])\n results.append(list2[i])\n if len(list1) > len(list2):\n <FILL_HERE>\n else:\n results.extend(list2[i+1:])\n return results", | |
] | |
def process_example(args): | |
for x in generate(args): | |
pass | |
return x | |
css = ".generating {visibility: hidden}" | |
monospace_css = """ | |
#q-input textarea { | |
font-family: monospace, 'Consolas', Courier, monospace; | |
} | |
""" | |
css += share_btn_css + monospace_css + ".gradio-container {color: black}" | |
description = """ | |
<div style="text-align: center;"> | |
<h1> ⭐ StarCoder <span style='color: #e6b800;'>Models</span> Playground</h1> | |
</div> | |
<div style="text-align: left;"> | |
<p>This is a demo to generate text and code with the following StarCoder models:</p> | |
<ul> | |
<li><a href="https://huggingface.co/bigcode/starcoderplus" style='color: #e6b800;'>StarCoderPlus</a>: A finetuned version of StarCoderBase on English web data, making it strong in both English text and code generation.</li> | |
<li><a href="https://huggingface.co/bigcode/starcoderbase" style='color: #e6b800;'>StarCoderBase</a>: A code generation model trained on 80+ programming languages, providing broad language coverage for code generation tasks.</li> | |
<li><a href="https://huggingface.co/bigcode/starcoder" style='color: #e6b800;'>StarCoder</a>: A finetuned version of StarCoderBase specifically focused on Python, while also maintaining strong performance on other programming languages.</li> | |
</ul> | |
<p><b>Please note:</b> These models are not designed for instruction purposes. If you're looking for instruction or want to chat with a fine-tuned model, you can visit the <a href="https://huggingface.co/spaces/HuggingFaceH4/starchat-playground">StarChat Playground</a>.</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.](hf.co/bigcode)""" | |
with gr.Blocks(theme=theme, analytics_enabled=False, css=css) as demo: | |
with gr.Column(): | |
gr.Markdown(description) | |
with gr.Row(): | |
version = gr.Dropdown( | |
["StarCoderPlus", "StarCoderBase", "StarCoder"], | |
value="StarCoderPlus", | |
label="Model", | |
info="Choose a model from the list", | |
) | |
with gr.Row(): | |
with gr.Column(): | |
instruction = gr.Textbox( | |
placeholder="Enter your code 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") | |
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=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", | |
) | |
gr.Markdown(disclaimer) | |
with gr.Group(elem_id="share-btn-container"): | |
community_icon = gr.HTML(community_icon_html, visible=True) | |
loading_icon = gr.HTML(loading_icon_html, visible=True) | |
share_button = gr.Button( | |
"Share to community", elem_id="share-btn", visible=True | |
) | |
gr.Examples( | |
examples=examples, | |
inputs=[instruction], | |
cache_examples=False, | |
fn=process_example, | |
outputs=[output], | |
) | |
gr.Markdown(FORMATS) | |
submit.click( | |
generate, | |
inputs=[instruction, temperature, max_new_tokens, top_p, repetition_penalty, version], | |
outputs=[output], | |
) | |
share_button.click(None, [], [], _js=share_js) | |
demo.queue(concurrency_count=16).launch(debug=True) |