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README.md

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 ---
-title: Urdu Edu Reasoning
-emoji: 👁
-colorFrom: blue
-colorTo: purple
+title: Urdu Education & Reasoning
+emoji: ⚖️
+colorFrom: green
+colorTo: yellow
 sdk: gradio
-sdk_version: 6.19.0
-python_version: '3.13'
+sdk_version: 5.49.1
 app_file: app.py
 pinned: false
+license: gemma
+models:
+  - abdullah693/gemma-3-4b-it-urdu-edu-reasoning
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Urdu Education & Reasoning — Gemma-3-4B adapted via Adaption AutoScientist
+
+A results page + live demo for a Gemma-3-4B model adapted to Urdu by translating English
+knowledge corpora into Urdu with Adaption AutoScientist, benchmarked on UrduMMLU (46.2%, +1.3 vs base).
+
+**Hardware:** set Space → Settings → Hardware → **ZeroGPU** (PRO). The model loads bf16 on the H200 per request.

app.py

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+"""
+Urdu Education & Reasoning — Gemma-3-4B adapted to Urdu via Adaption AutoScientist.
+A compelling results page + live demo, served on HF ZeroGPU.
+"""
+import os, spaces, gradio as gr, torch
+from threading import Thread
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+
+MODEL = os.environ.get("MODEL_REPO", "abdullah693/gemma-3-4b-it-urdu-edu-reasoning")
+
+print("loading model on cuda (module level for ZeroGPU)...", flush=True)
+tokenizer = AutoTokenizer.from_pretrained(MODEL)
+model = AutoModelForCausalLM.from_pretrained(MODEL, device_map="cuda", torch_dtype=torch.bfloat16)
+model.eval()
+print("ready", flush=True)
+
+
+@spaces.GPU(duration=120)
+def respond(message, history, max_tokens, temperature):
+    msgs = []
+    for u, a in history:
+        msgs.append({"role": "user", "content": u})
+        if a: msgs.append({"role": "assistant", "content": a})
+    msgs.append({"role": "user", "content": message})
+    enc = tokenizer.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt",
+                                        return_dict=True).to(model.device)
+    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+    Thread(target=model.generate, kwargs=dict(**enc, streamer=streamer, max_new_tokens=int(max_tokens),
+           do_sample=temperature > 0, temperature=temperature if temperature > 0 else None,
+           repetition_penalty=1.1, pad_token_id=tokenizer.pad_token_id or tokenizer.eos_token_id)).start()
+    out = ""
+    for t in streamer:
+        out += t; yield out
+
+
+CSS = """
+@import url('https://fonts.googleapis.com/css2?family=Playfair+Display:wght@700&family=Inter:wght@400;500;600&display=swap');
+.gradio-container{max-width:1000px!important;margin:0 auto!important}
+#hero{position:relative;border-radius:20px;overflow:hidden;margin-bottom:14px;
+  background:linear-gradient(135deg,#01411C 0%,#0a7a43 55%,#01411C 100%);box-shadow:0 14px 40px rgba(1,40,20,.35)}
+#hero .in{padding:38px 26px;text-align:center;color:#fff}
+#hero h1{font-family:'Playfair Display',serif;font-size:2.4rem;margin:.1em 0;color:#fff;line-height:1.1}
+#hero h1 .a{color:#e9c75a}
+#hero p{font-family:'Inter',sans-serif;font-size:1.05rem;color:#fff;opacity:1;max-width:680px;margin:10px auto;text-shadow:0 1px 4px rgba(0,0,0,.4)}
+#hero .badges{display:flex;gap:8px;justify-content:center;flex-wrap:wrap;margin-top:14px}
+#hero .b{font-family:'Inter';font-size:.8rem;color:#06301c;background:#e9c75a;font-weight:600;padding:5px 13px;border-radius:999px}
+.sec{font-family:'Inter',sans-serif}
+.sec h2{font-family:'Playfair Display',serif;color:#01411C;font-size:1.5rem;margin:.2em 0 .1em}
+.kpi{display:flex;gap:10px;flex-wrap:wrap;justify-content:center;margin:6px 0 2px}
+.kpi .c{background:#f3f8f4;border:1px solid #d8e6dc;border-radius:14px;padding:12px 18px;text-align:center;min-width:120px}
+.kpi .v{font-size:1.7rem;font-weight:700;color:#0a7a43;font-family:'Inter'}
+.kpi .l{font-size:.78rem;color:#555}
+.note{background:#fbf6e9;border:1px solid #ecdfb8;border-radius:12px;padding:12px 16px;font-family:'Inter';font-size:.92rem;color:#4a4a4a}
+#foot{font-family:'Inter';font-size:.8rem;color:#888;text-align:center;margin-top:16px;line-height:1.7}
+#foot a{color:#0a7a43;text-decoration:none;font-weight:500}
+"""
+
+HERO = """<div id="hero"><div class="in">
+<h1>اردو <span class="a">Education & Reasoning</span></h1>
+<p>A Gemma-3-4B model adapted to Urdu by translating English knowledge corpora into Urdu with
+<b>Adaption AutoScientist</b> — and benchmarked on UrduMMLU.</p>
+<div class="badges"><span class="b">Gemma-3-4B</span><span class="b">Adaption AutoScientist</span>
+<span class="b">UrduMMLU 46.2%</span><span class="b">+1.3 vs base</span></div></div></div>"""
+
+KPI = """<div class="kpi">
+<div class="c"><div class="v">+5.9</div><div class="l">STEM (pts)</div></div>
+<div class="c"><div class="v">+3.6</div><div class="l">Profession</div></div>
+<div class="c"><div class="v">+2.7</div><div class="l">Social Sci.</div></div>
+<div class="c"><div class="v">46.2%</div><div class="l">UrduMMLU overall</div></div>
+<div class="c"><div class="v">beats</div><div class="l">base Gemma (44.96%)</div></div></div>"""
+
+APPROACH = """<div class="sec"><h2>How it was built</h2>
+<p>Most UrduMMLU domains test <b>transferable knowledge</b> (science, math, social science, reasoning) that
+exists in abundant English datasets. We assembled ~40K examples from open English corpora
+(MMLU, GSM8K, MATH, ARC, AQuA, commonsense) plus native-Urdu instruction data, then used
+<b>Adaption AutoScientist</b> and the <b>Adaptive Data</b> pipeline to adapt each example into natural
+Pakistani Urdu — adding richer prompts and English reasoning traces — and supervised-fine-tuned Gemma-3-4B on the result.</p></div>"""
+
+FINDING = """<div class="note">📌 <b>The honest finding.</b> Cross-lingual adaptation lifted <i>every transferable
+domain</i> and beat the base model. The one domain that didn't improve was <b>Urdu literature / Humanities</b>
+(��2.5) — knowledge that is intrinsic to the Urdu language and <i>cannot</i> be sourced by adapting English.
+Closing it needs <b>better native Urdu-literature datasets</b>, not better adaptation — a data-availability
+problem, not a method problem.</div>"""
+
+FOOT = """<div id="foot">Model: <a href="https://huggingface.co/abdullah693/gemma-3-4b-it-urdu-edu-reasoning" target="_blank">abdullah693/gemma-3-4b-it-urdu-edu-reasoning</a>
+ · Adapted with <b>Adaption AutoScientist</b> · Eval: <a href="https://huggingface.co/datasets/MBZUAI/UrduMMLU" target="_blank">UrduMMLU</a> (Urdu, 0-shot)<br>
+<i>Research/educational use — not authoritative for exams or religious rulings.</i></div>"""
+
+EXAMPLES = [
+    "پاکستان کا قومی پھول کون سا ہے اور اس کی کیا خصوصیات ہیں؟",
+    "اگر ایک ٹرین 60 کلومیٹر فی گھنٹہ کی رفتار سے 2.5 گھنٹے چلے تو کتنا فاصلہ طے کرے گی؟",
+    "تعلیم کسی معاشرے کی ترقی میں کیا کردار ادا کرتی ہے؟ مختصر وضاحت کریں۔",
+    "نظامِ شمسی میں کتنے سیارے ہیں اور سب سے بڑا سیارہ کون سا ہے؟",
+]
+
+with gr.Blocks(title="Urdu Education & Reasoning", theme=gr.themes.Soft(primary_hue="emerald"), css=CSS) as demo:
+    gr.HTML(HERO)
+    gr.HTML('<div class="sec"><h2>The result</h2></div>')
+    gr.HTML(KPI)
+    with gr.Row():
+        gr.Image("assets/fig_overall.png", show_label=False, container=False)
+        gr.Image("assets/fig_domains.png", show_label=False, container=False)
+    gr.HTML(APPROACH)
+    gr.Image("assets/fig_data.png", show_label=False, container=False)
+    gr.HTML(FINDING)
+    gr.HTML('<div class="sec"><h2>Try it — اردو میں سوال پوچھیں</h2></div>')
+    with gr.Accordion("⚙️ settings", open=False):
+        mt = gr.Slider(64, 512, value=256, step=32, label="Max new tokens")
+        tp = gr.Slider(0.0, 1.0, value=0.0, step=0.1, label="Temperature")
+    gr.ChatInterface(respond, additional_inputs=[mt, tp], examples=[[e] for e in EXAMPLES], cache_examples=False)
+    gr.HTML(FOOT)
+
+if __name__ == "__main__":
+    demo.queue().launch()

requirements.txt

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+torch
+transformers>=4.50
+accelerate
+sentencepiece
+gradio>=5.0
+spaces