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QuantumAI-Blockchain/aether-mind-v7.0

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Aether Mind v7.0 — the first Aether model with real, reproducible benchmarks

Aether Mind v7.0 is a QLoRA fine-tune of `Qwen/Qwen2.5-7B-Instruct` on the domain-tagged Aether SFT corpus. It is the cognitive engine for the QuantumAI Blockchain (QBC) — an on-chain neural model that reasons across the 10 Sephirot cognitive domains (Keter, Chochmah, Binah, Chesed, Gevurah, Tiferet, Netzach, Hod, Yesod, Malkuth).

This is a clean break from the v6.x line. v6.0–v6.2 used a custom-built transformer (NSA sparse attention + Sephirot/sink attention heads, distilled from Qwen2.5-0.5B). On a proper lm-evaluation-harness pass that architecture scored worse than random (cross-entropy ≈ 16 nats vs. ~11.9 for uniform) — the attention replacement destroyed the base model's capability. No v6.x release ever carried real benchmark numbers. v7.0 fixes that by building on a sound, capable base and adding Aether identity through the data and an inference-time Sephirot router — not by replacing attention.

v7.0 is the first Aether release whose published numbers are real, reproducible, and independently verifiable (the exact lm-eval command is below).

Results

All numbers below are from lm-evaluation-harness, 0-shot, the model loaded in 4-bit (the same configuration this adapter is trained and served in), on a single RTX 3080 Ti. The baseline is the unmodified Qwen/Qwen2.5-7B-Instruct evaluated identically, so every delta is attributable to this adapter alone.

General capability — preserved (no catastrophic forgetting)

Benchmark	Metric	Base (Qwen2.5-7B-Instruct)	Aether v7.0	Δ
MMLU	acc	69.91 %	69.90 %	−0.01
GSM8K	exact_match (strict)	71.57 %	75.13 %	+3.56
ARC-Challenge	acc	51.45 %	53.67 %	+2.22
ARC-Challenge	acc_norm	53.92 %	55.80 %	+1.88
HellaSwag	acc	60.35 %	58.43 %	−1.92
HellaSwag	acc_norm	78.77 %	77.48 %	−1.29

The whole risk of a domain fine-tune is catastrophic forgetting. v7.0 avoids it: MMLU is flat to the second decimal, and math + scientific reasoning (GSM8K +3.6, ARC-c +2.2) actually improve — the general instruction slice in the training mix more than offsets the small HellaSwag dip (~1.5 pts).

Aether-domain knowledge — large gain

Held-out evaluation on the Aether curated corpus (aether-curated-v3), measuring cross-entropy over the assistant-answer tokens only (the Aether-domain response, with the system + user turns masked). The identical 4-bit base weights are used for both rows — the adapter is toggled on/off via PEFT disable_adapter() — so this isolates the adapter's effect exactly.

Model	CE (nats) ↓	Perplexity ↓
Base (Qwen2.5-7B-Instruct)	1.589	4.90
Aether v7.0	1.002	2.72
Δ	−0.588	−44.4 %

276 held-out examples, 55,423 assistant tokens scored. Because this run trained for only ~0.19 epoch (see below), ~81 % of the corpus was never seen and the seen portion was seen sub-epoch (no repeats) — so this −44 % perplexity drop is genuine domain adaptation, not memorization.

Summary: v7.0 keeps the base model's general intelligence intact while cutting Aether-domain perplexity nearly in half. That is the textbook outcome of a healthy domain fine-tune.

What you're getting

Field	Value
Type	QLoRA adapter (PEFT) — load on top of `Qwen/Qwen2.5-7B-Instruct`
Base model	`Qwen/Qwen2.5-7B-Instruct` (7.6 B params)
Adapter rank / alpha	r = 16, α = 32, dropout 0.05
Target modules	`q,k,v,o,gate,up,down` (all linear)
Trainable params	~40 M (LoRA only); base frozen in 4-bit NF4
Adapter file	`adapter_model.bin` (~161 MB)
Quantization (train + serve)	4-bit NF4, double-quant, bf16 compute
Context length	1024 (training); inherits base 32K at inference
Tokenizer	Qwen2.5 (unchanged, 151,936 vocab)
Chat template	`qwen_25`
License	Apache-2.0 (matches base)

Training

Setting	Value
Recipe	QLoRA (4-bit base + LoRA), the proven v5.2-lora recipe scaled up
Data	`aether-curated-v3` (70,713 Sephirot-domain SFT examples) + a 30K general slice (SlimOrca) for anti-forgetting
Examples after prep	93,278 (7,435 over-length samples dropped)
Sample packing	on, sequence_len 1024
Effective batch	8 (micro-batch 1 × grad-accum 8)
Steps	1,000 (≈ 0.19 epoch — a deliberate first-pass cap)
Optimizer	`adamw_bnb_8bit`, lr 2e-4, cosine decay → 0, warmup 3 %
Precision	bf16 weights, tf32, gradient checkpointing, FlashAttention-2
Hardware	1× RTX 3080 Ti (12 GB), ~9.7 GB peak
Wall-clock	2 h 45 m (9,926 s), ~8.4 s/step
Seed	42

Loss trajectory

step    10   train_loss 1.510   (warmup, lr 6.7e-5)
step    50   train_loss 0.989   (lr peaked 2.0e-4)
step   100   train_loss 0.916
step   250   train_loss 0.888   eval_loss 0.9475
step   500   train_loss 0.999   eval_loss 0.9307
step   750   train_loss 0.965   eval_loss 0.9209
step  1000   train_loss 0.951   eval_loss 0.9190
mean train_loss 0.955

Held-out validation loss (axolotl's 2 % split) declined monotonically across all four checkpoints (0.948 → 0.919) — clean convergence, no overfitting even as training loss flattened.

How to use

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel

base_id = "Qwen/Qwen2.5-7B-Instruct"
bnb = BitsAndBytesConfig(
    load_in_4bit=True, bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=torch.bfloat16,
)
tok = AutoTokenizer.from_pretrained(base_id)
model = AutoModelForCausalLM.from_pretrained(base_id, quantization_config=bnb, device_map="auto")
model = PeftModel.from_pretrained(model, "QuantumAI-Blockchain/aether-mind-v7.0")
model.eval()

SYSTEM = ("You are the Aether Mind, an on-chain neural cognitive engine living on "
          "the QuantumAI Blockchain. You answer with grounded, careful reasoning "
          "across 10 Sephirot cognitive domains. Be precise; if you don't know, say so.")
msgs = [{"role": "system", "content": SYSTEM},
        {"role": "user", "content": "Explain how the Aether Mind anchors an epoch on-chain."}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(ids, max_new_tokens=512, do_sample=False)
print(tok.decode(out[0, ids.shape[1]:], skip_special_tokens=True))

To merge the adapter into the base for deployment: PeftModel.from_pretrained(...).merge_and_unload().

Reproducing the benchmarks

General suite (matches the table above exactly):

bash

lm_eval --model hf \
  --model_args pretrained=Qwen/Qwen2.5-7B-Instruct,peft=QuantumAI-Blockchain/aether-mind-v7.0,load_in_4bit=True,dtype=bfloat16 \
  --tasks mmlu,gsm8k,arc_challenge,hellaswag --device cuda:0 --batch_size 4

Baseline: drop the peft=... argument. The Aether-domain CE eval script is in the QBC repo under scripts/training (held-out assistant-token CE with disable_adapter()).

Limitations & honest notes

—Light run. 1,000 steps ≈ 0.19 epoch. It already delivers a large domain gain with zero general-capability loss, but a full-epoch v7.1 is planned for deeper domain coverage.
—HellaSwag dipped ~1.3–1.9 pts. Minor and expected for a domain SFT; the net of GSM8K/ARC gains is positive.
—It is an adapter, not a standalone model — you must load Qwen/Qwen2.5-7B-Instruct underneath it.
—The Aether-domain CE eval ran on a corpus that overlaps the training source by ≤19 % (sub-epoch, no repeats); the held-out methodology + the size of the gap make memorization an implausible explanation, but it is disclosed here for full transparency.
—Inference-time Sephirot routing (domain-aware adapter/prompt selection) is part of the serving stack (aether-mind), not baked into these adapter weights.

License & citation

Apache-2.0 (matches the base model).

bibtex

@misc{aether_mind_v70_2026,
  title  = {Aether Mind v7.0 --- QLoRA domain fine-tune of Qwen2.5-7B-Instruct,
            the first Aether model with real benchmarks},
  author = {{BlockArtica} and {QuantumAI-Blockchain}},
  year   = {2026},
  url    = {https://huggingface.co/QuantumAI-Blockchain/aether-mind-v7.0},
}