luna-playground / app.py
thomwolf's picture
thomwolf HF staff
Updating temperature
d471812
raw
history blame
7.76 kB
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 = os.environ.get("API_URL")
with open("./HHH_prompt.txt", "r") as f:
HHH_PROMPT = f.read() + "\n\n"
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 in addition to the pure code data it is formatted with special tokens. E.g. prefixes specifying the source of the file or special tokens separating code from a commit message. See below:
### Chat mode
Chat mode prepends the [HHH prompt](https://gist.github.com/jareddk/2509330f8ef3d787fc5aaac67aab5f11#file-hhh_prompt-txt) from Anthropic to the request which conditions the model to be an assistant.
### Prefixes
Any combination of the three following prefixes can be found in pure code files:
```
<reponame>REPONAME<filename>FILENAME<gh_stars>STARS\ncode<|endoftext|>
```
STARS can be one of: 0, 1-10, 10-100, 100-1000, 1000+
### Commits
The commits data is formatted as follows:
```
<commit_before>code<commit_msg>text<commit_after>code<|endoftext|>
```
### Jupyter structure
Jupyter notebooks were both trained in form of Python scripts as well as the following structured format:
```
<start_jupyter><jupyter_text>text<jupyter_code>code<jupyter_output>output<jupyter_text>
```
### Issues
We also trained on GitHub issues using the following formatting:
```
<issue_start><issue_comment>text<issue_comment>...<issue_closed>
```
### Fill-in-the-middle
Fill in the middle requires rearranging the model inputs. The playground does 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}"},
)
def generate(prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0, chat_mode=False):
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 chat_mode:
generate_kwargs.update({"stop_sequences": ["\nHuman", "\n-----"]})
if chat_mode and FIM_INDICATOR in prompt:
raise ValueError("Chat mode and FIM are mutually exclusive. Choose one or the other.")
if chat_mode:
chat_prompt = "Human: " + prompt + "\n\nAssistant:"
prompt = HHH_PROMPT + chat_prompt
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}"
stream = client.generate_stream(prompt, **generate_kwargs)
if fim_mode:
output = prefix
elif chat_mode:
output = chat_prompt
else:
output = prompt
previous_token = ""
for response in stream:
if fim_mode and response.token.text =="<|endoftext|>":
output += (suffix + "\n" + response.token.text)
elif chat_mode and response.token.text in ["Human", "-----"] and previous_token=="\n":
return output
else:
output += response.token.text
previous_token = response.token.text
yield output
return output
examples = [
"def print_hello_world():",
'def fibonacci(n: int) -> int:\n """ Compute the n-th Fibonacci number. """',
'from typing import List, Tuple\n\ndef sum_and_product(numbers: List[int]) -> Tuple[int, int]:\n """ Return the sum and the product of the integers in the list as a tuple. Here is the answer of the exercise"""',
"class ComplexNumbers:"
]
def process_example(args):
for x in generate(args):
pass
return x
css = ".generating {visibility: hidden}" + share_btn_css
with gr.Blocks(theme=theme, analytics_enabled=False, css=css) as demo:
with gr.Column():
gr.Markdown(
"""\
# BigCode - Playground
_Note:_ this is an internal playground - please do not share. The deployment can also change and thus the space not work as we continue development.\
"""
)
with gr.Row():
with gr.Column(scale=3):
instruction = gr.Textbox(placeholder="Enter your prompt here", label="Prompt", elem_id="q-input")
submit = gr.Button("Generate", variant="primary")
output = gr.Code(elem_id="q-output")
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)
with gr.Column(scale=1):
chat_mode = gr.Checkbox(
value=False,
label="Chat mode",
info="Uses Anthropic's HHH prompt to turn the model into an assistant."
)
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",
)
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",
)
submit.click(generate, inputs=[instruction, temperature, max_new_tokens, top_p, repetition_penalty, chat_mode], outputs=[output])
# instruction.submit(generate, inputs=[instruction, temperature, max_new_tokens, top_p, repetition_penalty, chat_mode], outputs=[output])
share_button.click(None, [], [], _js=share_js)
demo.queue(concurrency_count=16).launch(debug=True)