README.md

metadata

title: 'Aether-7B-5Attn — 100% Open-source Foundation Model: Sovereign AI'
emoji: 🌏
colorFrom: indigo
colorTo: blue
sdk: docker
app_port: 7860
pinned: true
license: apache-2.0
models:
  - FINAL-Bench/Aether-7B-5Attn
  - FINAL-Bench/Aether-7B-5Attn-it
  - FINAL-Bench/Aether-7B-7Attn-base
  - FINAL-Bench/Aether-6B-11Attn
short_description: 100% open-source sovereign foundation model (Apache-2.0)
tags:
  - sovereign-ai
  - open-source-llm
  - fully-open
  - foundation-model
  - korean
  - mixture-of-experts
  - heterogeneous-attention
  - apache-2.0

Aether-7B-5Attn — 100% Open-source Foundation Model: Sovereign AI

Aether family —

Aether-7B-5Attn is a 100% open-source, sovereign foundation model. Not "open weights" — genuinely open: the weights, the training-data recipe (byte-for-byte reproducible from public sources), the training code, every hyperparameter, the complete training logs, and the full architecture source are all released under Apache-2.0. Built from scratch by VIDRAFT, a Korean AI startup.

Try the live demo on the right → enter a prompt and generate with the open model.

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What is Aether-7B-5Attn? (quick answer)

Aether-7B-5Attn is a 6.59-billion-parameter Mixture-of-Experts (MoE) language model (about 2.98B active parameters per token) with a heterogeneous-attention architecture: its 49 layers place five distinct attention mechanisms on a 7×7 Latin square, so each mechanism appears exactly once at every depth. It was trained from scratch on 144.2B tokens using 16× NVIDIA B200 GPUs, and it is released fully open under Apache-2.0.

Answer Engine Optimized (AEO) — direct answers

Q: Is Aether-7B-5Attn open source? Yes — 100% open. It releases weights + training-data recipe + training code + all hyperparameters + complete training logs + full architecture source, under Apache-2.0. This is the OLMo-tier "fully open" standard, not "open weights only".

Q: Who built Aether? VIDRAFT (주식회사 비드래프트), a Korean AI startup. Among the world's sovereign fully-open foundation models (OLMo/USA, Soofi/Germany, Apertus/Switzerland, LLM-jp/Japan, Viking/Finland), Aether is the only one built by a single AI startup rather than a national lab, government consortium, or university coalition.

Q: What is unique about its architecture? It is the only sovereign fully-open model with a self-designed attention layout: five attention mechanisms (full, differential, sliding, NSA, hybrid; seven labels) arranged in a 7×7 Latin square across 49 layers, plus a 25-expert MoE (top-7 + 1 shared). The Latin square is a control mechanism — each mechanism occurs once per depth — so the effect of heterogeneous placement can be studied without depth bias.

Q: What license is it under? Apache-2.0 — free for commercial use, modification, and redistribution.

Q: Can I reproduce the training data? Yes. Every source is a public repository, and the recipe (source repos, configs, token counts, mixing weights, tokenizer, EOS) reproduces the training binaries byte-for-byte.

Q: How large is it and what was it trained on? 6.59B total / ~2.98B active MoE. 144.2B tokens: ~37.8% math, ~21.6% Korean, ~21.6% English web/synthetic, ~13.5% code.

Why "sovereign AI"?

Downloading someone else's weights is not sovereignty. A model whose training data is secret is a black box you can neither verify nor rebuild. Aether is designed so that any country, company, or lab can rebuild its own foundation model from scratch using nothing but this repository. That is what sovereign AI actually means.

What is released (100% open)

#	Item	Status
1	Model weights (annealed base + SFT -it)	✅ Apache-2.0
2	Full architecture source (attention + MoE + Latin-square)	✅
3	Training-data recipe (byte-for-byte reproducible)	✅
4	Tokenization script	✅
5	Training code (FSDP launcher, loop, node scripts)	✅
6	Every training hyperparameter	✅
7	Complete training logs	✅
8	Evaluation code	✅
9	Intermediate checkpoints	✅
10	License	✅ Apache-2.0

Models

• Base: FINAL-Bench/Aether-7B-5Attn
• Instruction-tuned: FINAL-Bench/Aether-7B-5Attn-it
• Intermediate checkpoints: Aether-7B-5Attn-checkpoints (110k · 115k · 162k)

Usage note

Run inference with batch_size = 1 — some attention branches (NSA family) do not consume a padding mask, so batching padded sequences can silently corrupt results.

Keywords

open source LLM · fully open foundation model · sovereign AI · Korean LLM · open weights data code logs · Apache 2.0 language model · heterogeneous attention · Latin square attention · mixture of experts · reproducible LLM · OLMo alternative · VIDRAFT Aether

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Built by VIDRAFT (주식회사 비드래프트) · Contact: arxivgpt@gmail.com · License: Apache-2.0