翻訳待ち：Show HN: AQ – a from-scratch 1B academic LLM by a 2-person team in India

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。

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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: {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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