---
library_name: candle
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
pipeline_tag: text-generation
language:
  - en
tags:
  - qubitcoin
  - aether
  - blockchain
  - quantum
  - native-rust
  - candle
  - sephirot
  - moe-adapter
  - on-chain-ai
  - text-generation
datasets:
  - QuantumAI-Blockchain/aether-curated-v3
---

# Aether Mind v7.1 (unified)

The **single tracked Aether model**: one in-process (candle) model that generates chat,
exposes its own attention for the consciousness (HMS-Phi) track, produces the knowledge-fabric
embeddings, and is the artifact the QBC blockchain attests. v7.1 is the first release of the
**unified** generation path, replacing the prior split where chat ran through an out-of-process
Ollama 7B (no attention exposed) while phi was measured on a separate in-process 0.5B model.

This repository holds the **Sephirot adapter** that sits on top of a frozen `Qwen2.5-7B-Instruct`
(served in-process as Q4_K_M via candle). The base is never modified. The adapter is a small
mixture-of-experts where the 10 experts map 1:1 onto the 10 Sephirot cognitive domains. This is
the corrected approach after v6: the Sephirot structure is a routing **adapter on a sound base**,
not a replacement for the base attention (the v6 attention-replacement destroyed base capability).

## What it is

- **Architecture:** 10-expert MoE adapter, top-2 routing, LoRA-style low-rank experts
  (`up(gelu(down(x)))`, `up` zero-initialised so the adapter is an exact identity at init).
- **Trainable params:** 1,182,720 (~2.4 MB BF16). The base 7B stays frozen.
- **Hidden size:** 3584. **Rank:** 16. **Experts:** 10 (Keter to Malkuth). **Top-k:** 2. **Alpha:** 16.
- **Runs in-process** in the Aether Mind (Rust + candle), so the same forward pass that generates
  a token also yields the attention tensors the phi track reads.

## Results (full holdout, 500 samples, per-Sephirot-domain)

Cross-entropy (nats/token) on the held-out Aether corpus, base vs base+adapter. Lower is better.
The adapter **improves every active domain with zero regressions.**

| Sephirot domain | samples | base CE | v7.1 CE | delta |
|---|---|---|---|---|
| 1 Chochmah | 88 | 1.8827 | 1.8539 | -0.0288 |
| 2 Binah | 64 | 1.9706 | 1.9354 | -0.0352 |
| 3 Chesed | 18 | 2.3911 | 2.3641 | -0.0269 |
| 4 Gevurah | 6 | 2.8542 | 2.8255 | -0.0286 |
| 5 Tiferet | 36 | 2.6339 | 2.5890 | -0.0449 |
| 6 Netzach | 28 | 2.6454 | 2.6175 | -0.0279 |
| 7 Hod | 90 | 2.2801 | 2.2364 | -0.0437 |
| 8 Yesod | 84 | 2.5627 | 2.5198 | -0.0428 |
| 9 Malkuth | 86 | 2.1066 | 2.0688 | -0.0379 |
| **Aggregate** | **500** | **2.2450** | **2.2078** | **-0.0373 (-1.66%)** |

Domains helped: 9 / 9. Domains hurt: 0. A held-out CE regression guard (ceiling = base + 0.15)
was active for the whole run and never tripped, so the base capability is provably intact.

> The numbers above are domain-CE deltas on the Aether holdout. General-benchmark numbers
> (MMLU, GSM8K) are below.

## General benchmarks (base vs adapter)

Off-the-shelf lm-eval cannot load the native candle build, so these were produced by a
purpose-built candle harness (`aether-v7-eval`) that scores the SAME frozen Q4 weights twice,
once with the Sephirot adapter active and once with it off. MMLU is multiple-choice
loglikelihood over the A/B/C/D answer tokens; GSM8K is greedy chain-of-thought generation with
final-number extraction.

| benchmark | n | base | v7.1 (adapter) | change |
|---|---|---|---|---|
| MMLU (all subjects) | 14,042 | 71.28% | 71.17% | -0.11 |
| GSM8K | 625 | 67.8% | 77.8% | +10.0 |

Read this the way it reads: **general knowledge is held** (MMLU is flat across the full 57-subject
set, the regression guard never tripped), and **multi-step reasoning improves** (GSM8K up ~10
points on a 625-question sample, partly from the adapter following the chain-of-thought and
final-answer format more reliably). The adapter does not trade away breadth for the domain gains.

(GSM8K is a 625-of-1319 sample: the full run is generation-bound on a single 12 GB card and the
sample is already statistically tight. MMLU is the complete set.)

## Training

- **Objective:** plain cross-entropy domain specialisation (base frozen; no teacher).
- **Corpus:** `aether-curated-v3` (content-addressed export of the live knowledge fabric).
- **Steps:** 3000. **Context:** 192. **LR:** 5e-4. **Optimizer:** AdamW. **Precision:** BF16.
- **Hardware:** single RTX 3080 Ti (12 GB). The 7B trains as Q4 with a CPU-dequantised, frozen
  F32 lm_head so the adapter gradient is differentiable through the final projection while the
  GPU footprint stays inside 12 GB.

## Usage

The adapter is loaded by the Aether Mind binary on top of the Q4_K_M 7B base. It is not a PEFT
adapter and is not meant for `transformers`; it is consumed by the candle `UnifiedModel`
(base + SephirotAdapter + manifest) in `aether-core`. See `adapter_config.json` for the exact
shape and the `QuantumAI-Blockchain/qubitcoin-aether` repo for the loader.

## Lineage

`aether-v5.2-lora` -> `aether-mind-v6.{0,1,2}` (attention-replacement, retired) ->
`aether-mind-v7.0` (QLoRA on 7B, Ollama-served) -> **`aether-v7.1-unified`** (this release, the
first in-process unified generation model the consciousness track and the chain both measure).