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| import os | |
| import gradio as gr | |
| from huggingface_hub import Repository | |
| from text_generation import Client | |
| # from dialogues import DialogueTemplate | |
| from share_btn import (community_icon_html, loading_icon_html, share_btn_css, | |
| share_js) | |
| HF_TOKEN = os.environ.get("HF_TOKEN", None) | |
| API_TOKEN = os.environ.get("API_TOKEN", None) | |
| API_URL = os.environ.get("API_URL", None) | |
| API_URL = "https://api-inference.huggingface.co/models/timdettmers/guanaco-33b-merged" | |
| client = Client( | |
| API_URL, | |
| headers={"Authorization": f"Bearer {API_TOKEN}"}, | |
| ) | |
| repo = None | |
| def get_total_inputs(inputs, chatbot, preprompt, user_name, assistant_name, sep): | |
| past = [] | |
| for data in chatbot: | |
| user_data, model_data = data | |
| if not user_data.startswith(user_name): | |
| user_data = user_name + user_data | |
| if not model_data.startswith(sep + assistant_name): | |
| model_data = sep + assistant_name + model_data | |
| past.append(user_data + model_data.rstrip() + sep) | |
| if not inputs.startswith(user_name): | |
| inputs = user_name + inputs | |
| total_inputs = preprompt + "".join(past) + inputs + sep + assistant_name.rstrip() | |
| return total_inputs | |
| def has_no_history(chatbot, history): | |
| return not chatbot and not history | |
| header = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions." | |
| prompt_template = "### Human: {query}\n### Assistant:{response}" | |
| def generate( | |
| user_message, | |
| chatbot, | |
| history, | |
| temperature, | |
| top_p, | |
| max_new_tokens, | |
| repetition_penalty, | |
| ): | |
| # Don't return meaningless message when the input is empty | |
| if not user_message: | |
| print("Empty input") | |
| history.append(user_message) | |
| past_messages = [] | |
| for data in chatbot: | |
| user_data, model_data = data | |
| past_messages.extend( | |
| [{"role": "user", "content": user_data}, {"role": "assistant", "content": model_data.rstrip()}] | |
| ) | |
| if len(past_messages) < 1: | |
| prompt = header + prompt_template.format(query=user_message, response="") | |
| else: | |
| prompt = header | |
| for i in range(0, len(past_messages), 2): | |
| intermediate_prompt = prompt_template.format(query=past_messages[i]["content"], response=past_messages[i+1]["content"]) | |
| print("intermediate: ", intermediate_prompt) | |
| prompt = prompt + '\n' + intermediate_prompt | |
| prompt = prompt + prompt_template.format(query=user_message, response="") | |
| generate_kwargs = { | |
| "temperature": temperature, | |
| "top_p": top_p, | |
| "max_new_tokens": max_new_tokens, | |
| } | |
| 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, | |
| truncate=999, | |
| seed=42, | |
| ) | |
| stream = client.generate_stream( | |
| prompt, | |
| **generate_kwargs, | |
| ) | |
| output = "" | |
| for idx, response in enumerate(stream): | |
| if response.token.text == '': | |
| break | |
| if response.token.special: | |
| continue | |
| output += response.token.text | |
| if idx == 0: | |
| history.append(" " + output) | |
| else: | |
| history[-1] = output | |
| chat = [(history[i].strip(), history[i + 1].strip()) for i in range(0, len(history) - 1, 2)] | |
| yield chat, history, user_message, "" | |
| return chat, history, user_message, "" | |
| examples = [ | |
| "A Llama entered in my garden, what should I do?" | |
| ] | |
| def clear_chat(): | |
| return [], [] | |
| def process_example(args): | |
| for [x, y] in generate(args): | |
| pass | |
| return [x, y] | |
| title = """<h1 align="center">Guanaco Playground 💬</h1>""" | |
| custom_css = """ | |
| #banner-image { | |
| display: block; | |
| margin-left: auto; | |
| margin-right: auto; | |
| } | |
| #chat-message { | |
| font-size: 14px; | |
| min-height: 300px; | |
| } | |
| """ | |
| with gr.Blocks(analytics_enabled=False, css=custom_css) as demo: | |
| gr.HTML(title) | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown( | |
| """ | |
| 💻 This demo showcases the Guanaco 33B model, released together with the paper [QLoRA](https://arxiv.org/abs/2305.14314) | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Box(): | |
| output = gr.Markdown() | |
| chatbot = gr.Chatbot(elem_id="chat-message", label="Chat") | |
| with gr.Row(): | |
| with gr.Column(scale=3): | |
| user_message = gr.Textbox(placeholder="Enter your message here", show_label=False, elem_id="q-input") | |
| with gr.Row(): | |
| send_button = gr.Button("Send", elem_id="send-btn", visible=True) | |
| clear_chat_button = gr.Button("Clear chat", elem_id="clear-btn", visible=True) | |
| with gr.Accordion(label="Parameters", open=False, elem_id="parameters-accordion"): | |
| temperature = gr.Slider( | |
| label="Temperature", | |
| value=0.7, | |
| minimum=0.0, | |
| maximum=1.0, | |
| step=0.1, | |
| interactive=True, | |
| info="Higher values produce more diverse outputs", | |
| ) | |
| top_p = gr.Slider( | |
| label="Top-p (nucleus sampling)", | |
| value=0.9, | |
| minimum=0.0, | |
| maximum=1, | |
| step=0.05, | |
| interactive=True, | |
| info="Higher values sample more low-probability tokens", | |
| ) | |
| max_new_tokens = gr.Slider( | |
| label="Max new tokens", | |
| value=1024, | |
| minimum=0, | |
| maximum=2048, | |
| step=4, | |
| interactive=True, | |
| info="The maximum numbers of new tokens", | |
| ) | |
| repetition_penalty = gr.Slider( | |
| label="Repetition Penalty", | |
| value=1.2, | |
| minimum=0.0, | |
| maximum=10, | |
| step=0.1, | |
| interactive=True, | |
| info="The parameter for repetition penalty. 1.0 means no penalty.", | |
| ) | |
| with gr.Row(): | |
| gr.Examples( | |
| examples=examples, | |
| inputs=[user_message], | |
| cache_examples=False, | |
| fn=process_example, | |
| outputs=[output], | |
| ) | |
| with gr.Row(): | |
| gr.Markdown( | |
| "Disclaimer: The model can produce factually incorrect output, and should not be relied on to produce " | |
| "factually accurate information. The model was trained on various public datasets; while great efforts " | |
| "have been taken to clean the pretraining data, it is possible that this model could generate lewd, " | |
| "biased, or otherwise offensive outputs.", | |
| elem_classes=["disclaimer"], | |
| ) | |
| with gr.Row(): | |
| gr.Markdown( | |
| "[Privacy policy](https://gist.github.com/samhavens/c29c68cdcd420a9aa0202d0839876dac)", | |
| elem_classes=["disclaimer"], | |
| ) | |
| history = gr.State([]) | |
| last_user_message = gr.State("") | |
| user_message.submit( | |
| generate, | |
| inputs=[ | |
| user_message, | |
| chatbot, | |
| history, | |
| temperature, | |
| top_p, | |
| max_new_tokens, | |
| repetition_penalty, | |
| ], | |
| outputs=[chatbot, history, last_user_message, user_message], | |
| ) | |
| send_button.click( | |
| generate, | |
| inputs=[ | |
| user_message, | |
| chatbot, | |
| history, | |
| temperature, | |
| top_p, | |
| max_new_tokens, | |
| repetition_penalty, | |
| ], | |
| outputs=[chatbot, history, last_user_message, user_message], | |
| ) | |
| clear_chat_button.click(clear_chat, outputs=[chatbot, history]) | |
| demo.queue(concurrency_count=16).launch(debug=True) | |