app.py

import spaces
import gradio as gr
import torch
from transformers import pipeline, BitsAndBytesConfig, AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

MODEL_ID = "unsloth/Meta-Llama-3.1-70B-bnb-4bit"
ADAPTER_ID = "marcelbinz/Llama-3.1-Centaur-70B-adapter"

bnb_4bit_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model_base = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    device_map="auto",
    attn_implementation="flash_attention_2",
    quantization_config=bnb_4bit_config,
)

model = PeftModel.from_pretrained(model_base, ADAPTER_ID, device_map="auto")

pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    device_map="auto",
)

@spaces.GPU
def infer(prompt):
    return pipe(prompt, max_new_tokens=1, do_sample=True, temperature=1.0, return_full_text=True)[0]["generated_text"]

default_experiment = """You will take part in a Social Prediction Game.
You will observe a Player playing against an Opponent.
The Player and the Opponent simultaneously choose between option J and option Z.
Both parties win points based on their choices.
Your task is to predict the choices made by the Player.

The rules of the game are as follows:
If Player chooses option J and Opponent chooses option J, then Player wins 10 points and Opponent wins 10 points.
If Player chooses option J and Opponent chooses option Z, then Player wins 3 points and Opponent wins 12 points.
If Player chooses option Z and Opponent chooses option J, then Player wins 12 points and Opponent wins 3 points.
If Player chooses option Z and Opponent chooses option Z, then Player wins 5 points and Opponent wins 5 points.
You predict that Player will choose option <<
"""

with gr.Blocks(
    fill_width=True,
    css="""
    #prompt-box textarea {height:256px}
    """,
) as demo:

    # (optional) add a logo or hero image
    gr.Image(
        value="https://marcelbinz.github.io/imgs/centaur.png",
        show_label=False,
        height=180,
        container=False,
        elem_classes="mx-auto",          # centres the image
    )
    
    # ---------- NEW: info banner ----------
    gr.Markdown(
        """
        ### How to prompt:
        - We did not employ a particular prompt template – just phrase everything in natural language.  
        - Human choices are encapsulated by "<<" and ">>" tokens.
        - Most experiments in the training data are framed in terms of button presses. If possible, it is recommended to use that style.
        - You can find examples in the Supporting Information of our paper.
        """,
        elem_id="info-box",
    )

    inp = gr.Textbox(
        label="Prompt",
        elem_id="prompt-box",
        lines=16,
        max_lines=16,
        scale=3,
        value=default_experiment,
    )

    run = gr.Button("Run")
    run.click(infer, inp, inp)

demo.queue().launch()