AQ-1B — Academic Quotient v1 (Base)
AQ (Academic Quotient) — India's Concept-First Academic AI.
Raising the Academic Quotient of every student.
This is the
pretrained base model. For the tutor (instruct) model — the one that answers student questions — see
zyoralabs/AQ-academic-ai.
AQ-1B is a 1.26B-parameter foundation model built completely from scratch by Zyora Labs — proprietary architecture, own training code (pure PyTorch), own tokenizer, own data pipeline. No fine-tune of any existing model.
It is trained concept-first: the model learns the concepts of mathematics, physics, chemistry, biology, engineering, history, geography, civics and economics — from foundations to advanced — rather than curriculum checklists.
Highlights
- From scratch, end to end — architecture, tokenizer (32k byte-level BPE), training loop, and data pipeline all built in-house
- 20B tokens of knowledge-dense pretraining: encyclopedic text, real textbooks and course notes, scientific papers, and mathematical reasoning corpora
- Final quality anneal — the last 1.5B tokens use only the highest-quality sources (textbooks, course material, scientific papers, encyclopedic facts) with learning rate annealed to zero
- Tamil + Hindi inclusive — trained with native Tamil and Hindi text alongside English
- Progressive growth training — grown and continually trained through 75M → 300M → 1.26B parameter stages, each stage inheriting the previous stage's knowledge
Architecture (proprietary, from scratch)
| |
|---|
| Parameters | 1.26B |
| Layers | 48 |
| Hidden size | 1536 |
| Attention heads | 24 (grouped-query, 8 KV heads) |
| Feed-forward | SwiGLU, 4096 |
| Positional encoding | Rotary (RoPE) |
| Normalization | RMSNorm |
| Context length | 2048 |
| Vocabulary | 32,000 (byte-level BPE, English + Tamil + Hindi) |
| Embeddings | Tied |
Usage
1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3tok = AutoTokenizer.from_pretrained("zyoralabs/AQ-academic-ai-base")
4model = AutoModelForCausalLM.from_pretrained("zyoralabs/AQ-academic-ai-base", trust_remote_code=True)
5
6ids = tok("Photosynthesis is the process", return_tensors="pt").input_ids
7out = model.generate(ids, max_new_tokens=60)
8print(tok.decode(out[0]))
Intended use
AQ-1B is a base (pretrained) model — the foundation of the AQ educator stack (instruct tuning, retrieval grounding, and the AQ Playground sit on top of it). As a raw base model it predicts text continuations; it is not yet instruction-tuned.
Team
| Name | Role | Affiliation |
|---|
| Vasanth | Chief AI Researcher | Zyora Labs |
| Adithi Sreedhar | Jr AI Engineer | AI & DS, Arunachala College of Engineering for Women |
About
Built in India by
Zyora Labs. AQ v1 is the first release of the Academic Quotient model family.
