zyoralabs / AQ-academic-ai like 0

AQ-Academic-AI — Academic Quotient v1 (Tutor)

AQ (Academic Quotient) — India's Concept-First Academic AI. v1 now live. 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

1. 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). 2. 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:
{your question}
Tutor:

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("zyoralabs/AQ-academic-ai")
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"
ids = tok(prompt, return_tensors="pt").input_ids
out = model.generate(ids, max_new_tokens=200, do_sample=True,
                     temperature=0.7, top_k=40, repetition_penalty=1.3)
print(tok.decode(out[0][ids.shape[1]:]))

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) |

Benchmarks (0-shot, lm-evaluation-harness)

| Benchmark | AQ v1 Tutor | Notes | | ------------- | ----------- | ------------------------------------------------- | | SciQ | 69.2 | strong science knowledge for the size/data budget | | PIQA | 62.3 | | | ARC-easy | 45.1 | | | Winogrande | 49.5 | | | HellaSwag | 29.6 | | | MMLU | 25.2 | at-chance, like all \~1B-class models | | ARC-challenge | 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 educator stack. Best used with the Student/Tutor prompt format, sampling enabled, and (in production) retrieval grounding over curriculum material.

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.

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Model size

1B params

Tensor type

BF16

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