import json import os import shutil 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") FIM_PREFIX = "" FIM_MIDDLE = "" FIM_SUFFIX = "" FIM_INDICATOR = "" 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: ### Prefixes Any combination of the three following prefixes can be found in pure code files: ``` REPONAMEFILENAMESTARS\ncode<|endoftext|> ``` STARS can be one of: 0, 1-10, 10-100, 100-1000, 1000+ ### Commits The commits data is formatted as follows: ``` codetextcode<|endoftext|> ``` ### Jupyter structure Jupyter notebooks were both trained in form of Python scripts as well as the following structured format: ``` textcodeoutput ``` ### Issues We also trained on GitHub issues using the following formatting: ``` text... ``` ### 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 beforecode 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): 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: 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 else: output = prompt for response in stream: if fim_mode and response.token.text =="<|endoftext|>": output += (suffix + "\n" + response.token.text) else: output += response.token.text yield output return output examples = [ "def print_hello_world():", "def fibonacci(n):", "class TransformerDecoder(nn.Module):", "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): temperature = gr.Slider( label="Temperature", value=0.2, minimum=0.0, maximum=2.0, step=0.1, 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], outputs=[output]) instruction.submit(generate, inputs=[instruction, temperature, max_new_tokens, top_p, repetition_penalty], outputs=[output]) share_button.click(None, [], [], _js=share_js) demo.queue(concurrency_count=16).launch(debug=True)