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| """ | |
| WFGY Space – tiny-GPT-2 variance-gate demo | |
| ★ 10 k GitHub ⭐ before 2025-08-01 unlocks WFGY 2.0 ★ | |
| """ | |
| import io, numpy as np, pandas as pd, gradio as gr | |
| from PIL import Image | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| from wfgy_sdk import get_engine | |
| from wfgy_sdk.evaluator import compare_logits, plot_histogram, softmax | |
| # tiny model (CPU) | |
| tok = AutoTokenizer.from_pretrained("sshleifer/tiny-gpt2") | |
| mdl = AutoModelForCausalLM.from_pretrained("sshleifer/tiny-gpt2") | |
| eng = get_engine() | |
| # paper benchmarks | |
| bench = pd.DataFrame({ | |
| "Benchmark": ["MMLU","GSM8K","BBH","MathBench","TruthfulQA", | |
| "XNLI","MLQA","LongBench","VQAv2","OK-VQA"], | |
| "Baseline": [61,78,79.3,72.2,62.4,59.5,78.1,51.4,69.1,65.7], | |
| "WFGY": [89.8,98.7,100.7,87.4,90.4,77.3,106.6,69.6,86.6,86.8] | |
| }) | |
| bench["Abs_gain"] = (bench["WFGY"] - bench["Baseline"]).round(1) | |
| bench["Rel_gain%"] = ((bench["Abs_gain"] / bench["Baseline"]) * 100).round(0) | |
| bench_sty = ( | |
| bench.style | |
| .background_gradient(cmap="Greens", subset=["Abs_gain","Rel_gain%"]) | |
| .format({"Abs_gain":"{:.1f}","Rel_gain%":"{:.0f}"}) | |
| ) | |
| # banner markdown | |
| banner = """ | |
| **📈 WFGY: One Click to Activate the AI Taiji Cycle** | |
| **📊 Semantic Accuracy ↑ 22.4 % | Reasoning Success ↑ 42.1 % | Stability ↑ 3.6 ×** | |
| _No beliefs. Only experiments._ | |
| WFGY 1.0 has already proven itself. | |
| --- | |
| ### 📜 Tutorial: How to Awaken the Soul of Your AI | |
| **Step 1 — Download** ([PDF](https://zenodo.org/records/15630970)) | |
| **Step 2 — Feed the AI** (upload, or try [Gemini](https://gemini.google.com/)) | |
| **Step 3 — Give the Command** “**Answer using WFGY** + your question” | |
| Prompt examples: *TBD* | |
| **Step 4 — Integrate the SDK** ([GitHub](https://github.com/onestardao/WFGY)) | |
| --- | |
| 🌟 **Star Reminder** → [Star the repo](https://github.com/onestardao/WFGY) | |
| _10 k ⭐ before 2025-08-01 unlocks **WFGY 2.0**._ | |
| """ | |
| # inference | |
| def run(prompt: str): | |
| p = prompt.strip() | |
| if not p: | |
| return "", "", "-", None | |
| ids = tok(p, return_tensors="pt") | |
| raw_L = mdl(**ids).logits[0, -1].detach().cpu().numpy() | |
| I, G = np.random.randn(2, 256).astype(np.float32) | |
| mod_L = eng.run(I, G, raw_L) | |
| m = compare_logits(raw_L, mod_L) | |
| head = f"▼ var {m['var_drop']*100:.1f}% | KL {m['kl_divergence']:.3f} | top-1 {'kept' if m['top1'] else 'changed'}" | |
| def top5(logits): | |
| p = softmax(logits) | |
| idx = p.argsort()[-5:][::-1] | |
| lines = [f\"'{tok.decode(int(i)).strip()}': {p[i]:.2e}\" for i in idx] | |
| return "\\n".join(lines) | |
| raw_txt = top5(raw_L) | |
| mod_txt = top5(mod_L) | |
| fig = plot_histogram(raw_L, mod_L) | |
| buf = io.BytesIO(); fig.savefig(buf, format="png"); buf.seek(0) | |
| return raw_txt, mod_txt, head, Image.open(buf) | |
| # UI | |
| with gr.Blocks(title="WFGY variance-gate demo") as demo: | |
| gr.Markdown(banner) | |
| prompt = gr.Textbox(label="Prompt", value="Explain Schrödinger's cat") | |
| btn = gr.Button("🚀 Run") | |
| with gr.Row(): | |
| raw_box = gr.Textbox(label="Raw top-5 tokens", lines=6) | |
| mod_box = gr.Textbox(label="WFGY top-5 tokens", lines=6) | |
| metrics = gr.Markdown() | |
| img = gr.Image(label="Logit histogram") | |
| gr.Markdown("### Paper benchmarks (fixed values from WFGY 1.0)") | |
| gr.DataFrame(bench_sty, interactive=False, wrap=True) | |
| btn.click(run, prompt, [raw_box, mod_box, metrics, img]) | |
| if __name__ == "__main__": | |
| demo.queue(default_concurrency_limit=2).launch() | |