Create app.py

app.py

@@ -0,0 +1,317 @@
+import os
+
+import gradio as gr
+
+from text_generation import Client, InferenceAPIClient
+
+openchat_preprompt = (
+    "\n<human>: Hi!\n<bot>: My name is Bot, model version is 0.15, part of an open-source kit for "
+    "fine-tuning new bots! I was created by Together, LAION, and Ontocord.ai and the open-source "
+    "community. I am not human, not evil and not alive, and thus have no thoughts and feelings, "
+    "but I am programmed to be helpful, polite, honest, and friendly.\n"
+)
+
+
+def get_client(model: str):
+    if model == "togethercomputer/GPT-NeoXT-Chat-Base-20B":
+        return Client(os.getenv("OPENCHAT_API_URL"))
+    return InferenceAPIClient(model, token=os.getenv("HF_TOKEN", None))
+
+
+def get_usernames(model: str):
+    """
+    Returns:
+        (str, str, str, str): pre-prompt, username, bot name, separator
+    """
+    if model in ("OpenAssistant/oasst-sft-1-pythia-12b", "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5"):
+        return "", "<|prompter|>", "<|assistant|>", "<|endoftext|>"
+    if model == "togethercomputer/GPT-NeoXT-Chat-Base-20B":
+        return openchat_preprompt, "<human>: ", "<bot>: ", "\n"
+    return "", "User: ", "Assistant: ", "\n"
+
+
+def predict(
+    model: str,
+    inputs: str,
+    typical_p: float,
+    top_p: float,
+    temperature: float,
+    top_k: int,
+    repetition_penalty: float,
+    watermark: bool,
+    chatbot,
+    history,
+):
+    client = get_client(model)
+    preprompt, user_name, assistant_name, sep = get_usernames(model)
+
+    history.append(inputs)
+
+    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()
+
+    partial_words = ""
+
+    if model in ("OpenAssistant/oasst-sft-1-pythia-12b", "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5"):
+        iterator = client.generate_stream(
+            total_inputs,
+            typical_p=typical_p,
+            truncate=1000,
+            watermark=watermark,
+            max_new_tokens=500,
+        )
+    else:
+        iterator = client.generate_stream(
+            total_inputs,
+            top_p=top_p if top_p < 1.0 else None,
+            top_k=top_k,
+            truncate=1000,
+            repetition_penalty=repetition_penalty,
+            watermark=watermark,
+            temperature=temperature,
+            max_new_tokens=500,
+            stop_sequences=[user_name.rstrip(), assistant_name.rstrip()],
+        )
+
+    for i, response in enumerate(iterator):
+        if response.token.special:
+            continue
+
+        partial_words = partial_words + response.token.text
+        if partial_words.endswith(user_name.rstrip()):
+            partial_words = partial_words.rstrip(user_name.rstrip())
+        if partial_words.endswith(assistant_name.rstrip()):
+            partial_words = partial_words.rstrip(assistant_name.rstrip())
+
+        if i == 0:
+            history.append(" " + partial_words)
+        elif response.token.text not in user_name:
+            history[-1] = partial_words
+
+        chat = [
+            (history[i].strip(), history[i + 1].strip())
+            for i in range(0, len(history) - 1, 2)
+        ]
+        yield chat, history
+
+
+def reset_textbox():
+    return gr.update(value="")
+
+
+def radio_on_change(
+    value: str,
+    disclaimer,
+    typical_p,
+    top_p,
+    top_k,
+    temperature,
+    repetition_penalty,
+    watermark,
+):
+    if value in ("OpenAssistant/oasst-sft-1-pythia-12b", "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5"):
+        typical_p = typical_p.update(value=0.2, visible=True)
+        top_p = top_p.update(visible=False)
+        top_k = top_k.update(visible=False)
+        temperature = temperature.update(visible=False)
+        disclaimer = disclaimer.update(visible=False)
+        repetition_penalty = repetition_penalty.update(visible=False)
+        watermark = watermark.update(False)
+    elif value == "togethercomputer/GPT-NeoXT-Chat-Base-20B":
+        typical_p = typical_p.update(visible=False)
+        top_p = top_p.update(value=0.25, visible=True)
+        top_k = top_k.update(value=50, visible=True)
+        temperature = temperature.update(value=0.6, visible=True)
+        repetition_penalty = repetition_penalty.update(value=1.01, visible=True)
+        watermark = watermark.update(False)
+        disclaimer = disclaimer.update(visible=True)
+    else:
+        typical_p = typical_p.update(visible=False)
+        top_p = top_p.update(value=0.95, visible=True)
+        top_k = top_k.update(value=4, visible=True)
+        temperature = temperature.update(value=0.5, visible=True)
+        repetition_penalty = repetition_penalty.update(value=1.03, visible=True)
+        watermark = watermark.update(True)
+        disclaimer = disclaimer.update(visible=False)
+    return (
+        disclaimer,
+        typical_p,
+        top_p,
+        top_k,
+        temperature,
+        repetition_penalty,
+        watermark,
+    )
+
+
+title = """<h1 align="center">Large Language Model Chat API</h1>"""
+description = """Language models can be conditioned to act like dialogue agents through a conversational prompt that typically takes the form:
+```
+User: <utterance>
+Assistant: <utterance>
+User: <utterance>
+Assistant: <utterance>
+...
+```
+In this app, you can explore the outputs of multiple LLMs when prompted in this way.
+"""
+
+text_generation_inference = """
+<div align="center">Powered by: <a href=https://github.com/huggingface/text-generation-inference>Text Generation Inference</a></div>
+"""
+
+openchat_disclaimer = """
+<div align="center">Checkout the official <a href=https://huggingface.co/spaces/togethercomputer/OpenChatKit>OpenChatKit feedback app</a> for the full experience.</div>
+"""
+
+with gr.Blocks(
+    css="""#col_container {margin-left: auto; margin-right: auto;}
+                #chatbot {height: 520px; overflow: auto;}"""
+) as demo:
+    gr.HTML(title)
+    gr.Markdown(text_generation_inference, visible=True)
+    with gr.Column(elem_id="col_container"):
+        model = gr.Radio(
+            value="OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
+            choices=[
+                "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
+                "OpenAssistant/oasst-sft-1-pythia-12b",
+                # "togethercomputer/GPT-NeoXT-Chat-Base-20B",
+                "google/flan-t5-xxl",
+                "google/flan-ul2",
+                "bigscience/bloom",
+                "bigscience/bloomz",
+                "EleutherAI/gpt-neox-20b",
+            ],
+            label="Model",
+            interactive=True,
+        )
+
+        chatbot = gr.Chatbot(elem_id="chatbot")
+        inputs = gr.Textbox(
+            placeholder="Hi there!", label="Type an input and press Enter"
+        )
+        disclaimer = gr.Markdown(openchat_disclaimer, visible=False)
+        state = gr.State([])
+        b1 = gr.Button()
+
+        with gr.Accordion("Parameters", open=False):
+            typical_p = gr.Slider(
+                minimum=-0,
+                maximum=1.0,
+                value=0.2,
+                step=0.05,
+                interactive=True,
+                label="Typical P mass",
+            )
+            top_p = gr.Slider(
+                minimum=-0,
+                maximum=1.0,
+                value=0.25,
+                step=0.05,
+                interactive=True,
+                label="Top-p (nucleus sampling)",
+                visible=False,
+            )
+            temperature = gr.Slider(
+                minimum=-0,
+                maximum=5.0,
+                value=0.6,
+                step=0.1,
+                interactive=True,
+                label="Temperature",
+                visible=False,
+            )
+            top_k = gr.Slider(
+                minimum=1,
+                maximum=50,
+                value=50,
+                step=1,
+                interactive=True,
+                label="Top-k",
+                visible=False,
+            )
+            repetition_penalty = gr.Slider(
+                minimum=0.1,
+                maximum=3.0,
+                value=1.03,
+                step=0.01,
+                interactive=True,
+                label="Repetition Penalty",
+                visible=False,
+            )
+            watermark = gr.Checkbox(value=False, label="Text watermarking")
+
+    model.change(
+        lambda value: radio_on_change(
+            value,
+            disclaimer,
+            typical_p,
+            top_p,
+            top_k,
+            temperature,
+            repetition_penalty,
+            watermark,
+        ),
+        inputs=model,
+        outputs=[
+            disclaimer,
+            typical_p,
+            top_p,
+            top_k,
+            temperature,
+            repetition_penalty,
+            watermark,
+        ],
+    )
+
+    inputs.submit(
+        predict,
+        [
+            model,
+            inputs,
+            typical_p,
+            top_p,
+            temperature,
+            top_k,
+            repetition_penalty,
+            watermark,
+            chatbot,
+            state,
+        ],
+        [chatbot, state],
+    )
+    b1.click(
+        predict,
+        [
+            model,
+            inputs,
+            typical_p,
+            top_p,
+            temperature,
+            top_k,
+            repetition_penalty,
+            watermark,
+            chatbot,
+            state,
+        ],
+        [chatbot, state],
+    )
+    b1.click(reset_textbox, [], [inputs])
+    inputs.submit(reset_textbox, [], [inputs])
+
+    gr.Markdown(description)
+    demo.queue(concurrency_count=16).launch(debug=True)