zyoralabs/AQ-academic-ai · Hugging Face
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AQ-Academic-AI — Academic Quotient v1 (Tutor)
AQ (Academic Quotient) — India's Concept-First Academic AI. v1 now live.<br>Raising the Academic Quotient of every student.
AQ is a 1.26B-parameter academic tutor built completely from scratch by Zyora Labs — proprietary architecture, own training code (pure PyTorch), own tokenizer, own data pipeline, own tutor fine-tune. No fine-tune of any existing model.
This is the tutor (instruct) model : it answers student questions directly with explanations, numbered steps, and worked examples. The pretrained base model is available at zyoralabs/AQ-academic-ai-base.
Training
Pretraining — 20B tokens , knowledge-dense and concept-first: real textbooks, course notes, scientific papers, encyclopedic text, mathematical reasoning — in English, Tamil, and Hindi. Grown progressively 75M → 300M → 1.26B, finished with a quality anneal (LR → 0 on the highest-quality academic text).
Tutor fine-tune — 400M tokens of educator-style instruction data (explanations, step-by-step math, knowledge Q&A, Hindi instructions), loss masked to tutor responses.
Prompt format
### Student:<br>{your question}
### Tutor:
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("zyoralabs/AQ-academic-ai")<br>model = AutoModelForCausalLM.from_pretrained("zyoralabs/AQ-academic-ai", trust_remote_code=True)
prompt = "### Student:\nWhat is a stack in data structures?\n\n### Tutor:\n"<br>ids = tok(prompt, return_tensors="pt").input_ids<br>out = model.generate(ids, max_new_tokens=200, do_sample=True,<br>temperature=0.7, top_k=40, repetition_penalty=1.3)<br>print(tok.decode(out[0][ids.shape[1]:]))
Architecture (proprietary, from scratch)
Parameters<br>1.26B
Layers<br>48
Hidden size<br>1536
Attention heads<br>24 (grouped-query, 8 KV heads)
Feed-forward<br>SwiGLU, 4096
Positional encoding<br>Rotary (RoPE)
Normalization<br>RMSNorm
Context length<br>2048
Vocabulary<br>32,000 (byte-level BPE, English + Tamil + Hindi)
Benchmarks (0-shot, lm-evaluation-harness)
Benchmark<br>AQ v1 Tutor<br>Notes
SciQ<br>69.2<br>strong science knowledge for the size/data budget
PIQA<br>62.3
ARC-easy<br>45.1
Winogrande<br>49.5
HellaSwag<br>29.6
MMLU<br>25.2<br>at-chance, like all ~1B-class models
ARC-challenge<br>21.2
For context: models of this size trained on 15×–150× more tokens (e.g. 300B–3T) reach SciQ ~84–89. AQ reaches ~70 on just 20B tokens — the concept-first, knowledge-dense corpus is the point.
Transparency: as a 1.26B model, AQ v1 has real limits — multi-step arithmetic word problems and MMLU-style abstract reasoning are weak (these unlock at larger scale, on our roadmap). In the AQ product, answers are additionally grounded with retrieval over real study material.
Intended use
The tutor layer of the AQ...