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| import json | |
| import os | |
| 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("TRL_TOKEN", None) | |
| API_URL = os.environ.get("API_URL") | |
| 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"], | |
| ) | |
| if HF_TOKEN: | |
| repo = Repository( | |
| local_dir="data", clone_from="trl-lib/stack-llama-prompts", use_auth_token=HF_TOKEN, repo_type="dataset" | |
| ) | |
| repo.git_pull() | |
| client = Client( | |
| API_URL, | |
| headers={"Authorization": f"Bearer {HF_TOKEN}"}, | |
| ) | |
| PROMPT_TEMPLATE = """Question: {prompt}\n\nAnswer:""" | |
| def save_inputs_and_outputs(inputs, outputs, generate_kwargs): | |
| with open(os.path.join("data", "prompts.jsonl"), "a") as f: | |
| json.dump({"inputs": inputs, "outputs": outputs, "generate_kwargs": generate_kwargs}, f, ensure_ascii=False) | |
| f.write("\n") | |
| commit_url = repo.push_to_hub() | |
| def generate(instruction, temperature=0.9, max_new_tokens=256, top_p=0.95, top_k=100): | |
| formatted_instruction = PROMPT_TEMPLATE.format(prompt=instruction) | |
| temperature = float(temperature) | |
| top_p = float(top_p) | |
| generate_kwargs = dict( | |
| temperature=temperature, | |
| max_new_tokens=max_new_tokens, | |
| top_p=top_p, | |
| top_k=top_k, | |
| do_sample=True, | |
| truncate=999, | |
| seed=42, | |
| stop_sequences=["</s>"], | |
| ) | |
| stream = client.generate_stream( | |
| formatted_instruction, | |
| **generate_kwargs, | |
| ) | |
| output = "" | |
| for response in stream: | |
| output += response.token.text | |
| yield output | |
| if HF_TOKEN: | |
| print("Pushing prompt and completion to the Hub") | |
| save_inputs_and_outputs(formatted_instruction, output, generate_kwargs) | |
| return output | |
| examples = [ | |
| "A llama is in my lawn. How do I get rid of him?", | |
| "How do I create an array in C++ which contains all even numbers between 1 and 10?", | |
| "How can I sort a list in Python?", | |
| "How can I write a Java function to generate the nth Fibonacci number?", | |
| "How many helicopters can a llama eat in one sitting?", | |
| ] | |
| 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( | |
| """<h1><center>🦙🦙🦙 StackLLaMa 🦙🦙🦙</center></h1> | |
| StackLLaMa is a 7 billion parameter language model that has been trained on pairs of questions and answers from [Stack Exchange](https://stackexchange.com) using Reinforcement Learning from Human Feedback with the [TRL library](https://github.com/lvwerra/trl). For more details, check out our [blog post](https://huggingface.co/blog/stackllama). | |
| Type in the box below and click the button to generate answers to your most pressing questions 🔥! | |
| **Note:** we are collecting your prompts and model completions for research purposes. | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=3): | |
| instruction = gr.Textbox(placeholder="Enter your question here", label="Question", elem_id="q-input") | |
| with gr.Box(): | |
| gr.Markdown("**Answer**") | |
| output = gr.Markdown(elem_id="q-output") | |
| submit = gr.Button("Generate", variant="primary") | |
| 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=True, | |
| fn=process_example, | |
| outputs=[output], | |
| ) | |
| with gr.Column(scale=1): | |
| temperature = gr.Slider( | |
| label="Temperature", | |
| value=0.8, | |
| minimum=0.01, | |
| 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=2048, | |
| step=4, | |
| interactive=True, | |
| info="The maximum numbers of new tokens", | |
| ) | |
| top_p = gr.Slider( | |
| label="Top-p (nucleus sampling)", | |
| value=0.95, | |
| minimum=0.0, | |
| maximum=1, | |
| step=0.05, | |
| interactive=True, | |
| info="Higher values sample more low-probability tokens", | |
| ) | |
| top_k = gr.Slider( | |
| label="Top-k", | |
| value=40, | |
| minimum=0, | |
| maximum=100, | |
| step=2, | |
| interactive=True, | |
| info="Sample from top-k tokens", | |
| ) | |
| submit.click(generate, inputs=[instruction, temperature, max_new_tokens, top_p, top_k], outputs=[output]) | |
| instruction.submit(generate, inputs=[instruction, temperature, max_new_tokens, top_p, top_k], outputs=[output]) | |
| share_button.click(None, [], [], _js=share_js) | |
| demo.queue(concurrency_count=16).launch(debug=True) | |