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import json |
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import os |
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import shutil |
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import gradio as gr |
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from huggingface_hub import Repository |
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from text_generation import Client |
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from dialogues import DialogueTemplate |
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from share_btn import (community_icon_html, loading_icon_html, share_btn_css, |
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share_js) |
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HF_TOKEN = os.environ.get("HF_TOKEN", None) |
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API_TOKEN = os.environ.get("API_TOKEN", None) |
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API_URL = os.environ.get("API_URL", None) |
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client = Client( |
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API_URL, |
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headers={"Authorization": f"Bearer {API_TOKEN}"}, |
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) |
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if HF_TOKEN: |
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try: |
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shutil.rmtree("./data/") |
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except: |
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pass |
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repo = Repository( |
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local_dir="./data/", clone_from="HuggingFaceH4/starchat-prompts", use_auth_token=HF_TOKEN, repo_type="dataset" |
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) |
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repo.git_pull() |
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def save_inputs_and_outputs(inputs, outputs, generate_kwargs): |
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with open(os.path.join("data", "prompts.jsonl"), "a") as f: |
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json.dump({"inputs": inputs, "outputs": outputs, "generate_kwargs": generate_kwargs}, f, ensure_ascii=False) |
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f.write("\n") |
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repo.push_to_hub() |
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def get_total_inputs(inputs, chatbot, preprompt, user_name, assistant_name, sep): |
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past = [] |
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for data in chatbot: |
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user_data, model_data = data |
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if not user_data.startswith(user_name): |
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user_data = user_name + user_data |
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if not model_data.startswith(sep + assistant_name): |
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model_data = sep + assistant_name + model_data |
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past.append(user_data + model_data.rstrip() + sep) |
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if not inputs.startswith(user_name): |
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inputs = user_name + inputs |
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total_inputs = preprompt + "".join(past) + inputs + sep + assistant_name.rstrip() |
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return total_inputs |
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def has_no_history(chatbot, history): |
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return not chatbot and not history |
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def generate( |
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system_message, |
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user_message, |
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chatbot, |
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history, |
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temperature, |
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top_k, |
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top_p, |
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max_new_tokens, |
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repetition_penalty, |
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do_save=True, |
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): |
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if not user_message: |
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print("Empty input") |
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history.append(user_message) |
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past_messages = [] |
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for data in chatbot: |
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user_data, model_data = data |
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past_messages.extend( |
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[{"role": "user", "content": user_data}, {"role": "assistant", "content": model_data.rstrip()}] |
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) |
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if len(past_messages) < 1: |
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dialogue_template = DialogueTemplate( |
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system=system_message, messages=[{"role": "user", "content": user_message}] |
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) |
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prompt = dialogue_template.get_inference_prompt() |
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else: |
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dialogue_template = DialogueTemplate( |
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system=system_message, messages=past_messages + [{"role": "user", "content": user_message}] |
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) |
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prompt = dialogue_template.get_inference_prompt() |
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generate_kwargs = { |
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"temperature": temperature, |
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"top_k": top_k, |
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"top_p": top_p, |
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"max_new_tokens": max_new_tokens, |
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} |
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temperature = float(temperature) |
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if temperature < 1e-2: |
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temperature = 1e-2 |
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top_p = float(top_p) |
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generate_kwargs = dict( |
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temperature=temperature, |
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max_new_tokens=max_new_tokens, |
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top_p=top_p, |
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repetition_penalty=repetition_penalty, |
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do_sample=True, |
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truncate=999, |
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seed=42, |
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stop_sequences=["<|end|>"], |
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) |
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stream = client.generate_stream( |
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prompt, |
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**generate_kwargs, |
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) |
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output = "" |
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for idx, response in enumerate(stream): |
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if response.token.special: |
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continue |
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output += response.token.text |
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if idx == 0: |
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history.append(" " + output) |
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else: |
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history[-1] = output |
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chat = [(history[i].strip(), history[i + 1].strip()) for i in range(0, len(history) - 1, 2)] |
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yield chat, history, user_message, "" |
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if HF_TOKEN and do_save: |
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try: |
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print("Pushing prompt and completion to the Hub") |
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save_inputs_and_outputs(prompt, output, generate_kwargs) |
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except Exception as e: |
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print(e) |
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return chat, history, user_message, "" |
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examples = [ |
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"How can I write a Python function to generate the nth Fibonacci number?", |
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"How do I get the current date using shell commands? Explain how it works.", |
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"What's the meaning of life?", |
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"Write a function in Python to reverse words in a given string.", |
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] |
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def clear_chat(): |
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return [], [] |
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def process_example(args): |
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for [x, y] in generate(args): |
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pass |
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return [x, y] |
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title = """<h1 align="center">⭐ StarChat Playground 💬</h1>""" |
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custom_css = """ |
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#banner-image { |
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display: block; |
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margin-left: auto; |
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margin-right: auto; |
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} |
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#chat-message { |
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font-size: 14px; |
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min-height: 300px; |
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} |
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""" |
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with gr.Blocks(analytics_enabled=False, css=custom_css) as demo: |
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gr.HTML(title) |
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with gr.Row(): |
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with gr.Column(): |
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gr.Image("thumbnail.png", elem_id="banner-image", show_label=False) |
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with gr.Column(): |
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gr.Markdown( |
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""" |
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💻 This demo showcases an **alpha** version of **[StarChat](https://huggingface.co/HuggingFaceH4/starchat-alpha)**, a variant of **[StarCoderBase](https://huggingface.co/bigcode/starcoderbase)** that was fine-tuned on the [Dolly](https://huggingface.co/datasets/databricks/databricks-dolly-15k) and [OpenAssistant](https://huggingface.co/datasets/OpenAssistant/oasst1) datasets to act as a helpful coding assistant. The base model has 16B parameters and was pretrained on one trillion tokens sourced from 80+ programming languages, GitHub issues, Git commits, and Jupyter notebooks (all permissively licensed). |
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📝 For more details, check out our [blog post](). |
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⚠️ **Intended Use**: this app and its [supporting model](https://huggingface.co/HuggingFaceH4/starchat-alpha) are provided as educational tools to explain large language model fine-tuning; not to serve as replacement for human expertise. In particular, this alpha version of **StarChat** has not been aligned to human preferences with techniques like RLHF, so the model can produce problematic outputs (especially when prompted to do so). For more details on the model's limitations in terms of factuality and biases, see the [model card](https://huggingface.co/HuggingFaceH4/starchat-alpha#bias-risks-and-limitations). |
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⚠️ **Data Collection**: by default, we are collecting the prompts entered in this app to further improve and evaluate the model. Do **NOT** share any personal or sensitive information while using the app! You can opt out of this data collection by removing the checkbox below. |
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""" |
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) |
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with gr.Row(): |
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do_save = gr.Checkbox( |
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value=True, |
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label="Store data", |
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info="You agree to the storage of your prompt and generated text for research and development purposes:", |
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) |
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with gr.Accordion(label="System Prompt", open=False, elem_id="parameters-accordion"): |
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system_message = gr.Textbox( |
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elem_id="system-message", |
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placeholder="Below is a conversation between a human user and a helpful AI coding assistant.", |
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show_label=False, |
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) |
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with gr.Row(): |
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with gr.Box(): |
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output = gr.Markdown() |
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chatbot = gr.Chatbot(elem_id="chat-message", label="Chat") |
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with gr.Row(): |
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with gr.Column(scale=3): |
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user_message = gr.Textbox(placeholder="Enter your message here", show_label=False, elem_id="q-input") |
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with gr.Row(): |
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send_button = gr.Button("Send", elem_id="send-btn", visible=True) |
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clear_chat_button = gr.Button("Clear chat", elem_id="clear-btn", visible=True) |
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with gr.Accordion(label="Parameters", open=False, elem_id="parameters-accordion"): |
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temperature = gr.Slider( |
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label="Temperature", |
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value=0.2, |
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minimum=0.0, |
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maximum=1.0, |
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step=0.1, |
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interactive=True, |
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info="Higher values produce more diverse outputs", |
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) |
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top_k = gr.Slider( |
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label="Top-k", |
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value=50, |
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minimum=0.0, |
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maximum=100, |
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step=1, |
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interactive=True, |
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info="Sample from a shortlist of top-k tokens", |
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) |
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top_p = gr.Slider( |
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label="Top-p (nucleus sampling)", |
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value=0.95, |
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minimum=0.0, |
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maximum=1, |
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step=0.05, |
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interactive=True, |
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info="Higher values sample more low-probability tokens", |
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) |
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max_new_tokens = gr.Slider( |
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label="Max new tokens", |
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value=256, |
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minimum=0, |
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maximum=512, |
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step=4, |
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interactive=True, |
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info="The maximum numbers of new tokens", |
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) |
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repetition_penalty = gr.Slider( |
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label="Repetition Penalty", |
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value=1.2, |
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minimum=0.0, |
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maximum=10, |
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step=0.1, |
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interactive=True, |
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info="The parameter for repetition penalty. 1.0 means no penalty.", |
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) |
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with gr.Group(elem_id="share-btn-container"): |
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community_icon = gr.HTML(community_icon_html, visible=True) |
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loading_icon = gr.HTML(loading_icon_html, visible=True) |
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share_button = gr.Button("Share to community", elem_id="share-btn", visible=True) |
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with gr.Row(): |
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gr.Examples( |
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examples=examples, |
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inputs=[user_message], |
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cache_examples=False, |
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fn=process_example, |
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outputs=[output], |
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) |
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history = gr.State([]) |
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last_user_message = gr.State("") |
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user_message.submit( |
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generate, |
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inputs=[ |
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system_message, |
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user_message, |
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chatbot, |
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history, |
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temperature, |
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top_k, |
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top_p, |
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max_new_tokens, |
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repetition_penalty, |
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do_save, |
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], |
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outputs=[chatbot, history, last_user_message, user_message], |
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) |
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send_button.click( |
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generate, |
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inputs=[ |
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system_message, |
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user_message, |
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chatbot, |
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history, |
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temperature, |
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top_k, |
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top_p, |
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max_new_tokens, |
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repetition_penalty, |
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do_save, |
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], |
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outputs=[chatbot, history, last_user_message, user_message], |
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) |
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clear_chat_button.click(clear_chat, outputs=[chatbot, history]) |
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share_button.click(None, [], [], _js=share_js) |
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demo.queue(concurrency_count=16).launch(debug=True) |
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