Instructions to use QuantumAI-Blockchain/aether-v7.1-unified with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantumAI-Blockchain/aether-v7.1-unified with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantumAI-Blockchain/aether-v7.1-unified",
	filename="qwen2.5-7b-instruct-q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantumAI-Blockchain/aether-v7.1-unified with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantumAI-Blockchain/aether-v7.1-unified:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantumAI-Blockchain/aether-v7.1-unified:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantumAI-Blockchain/aether-v7.1-unified:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantumAI-Blockchain/aether-v7.1-unified:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf QuantumAI-Blockchain/aether-v7.1-unified:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantumAI-Blockchain/aether-v7.1-unified:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf QuantumAI-Blockchain/aether-v7.1-unified:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantumAI-Blockchain/aether-v7.1-unified:Q4_K_M

Use Docker

docker model run hf.co/QuantumAI-Blockchain/aether-v7.1-unified:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantumAI-Blockchain/aether-v7.1-unified with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantumAI-Blockchain/aether-v7.1-unified"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantumAI-Blockchain/aether-v7.1-unified",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantumAI-Blockchain/aether-v7.1-unified:Q4_K_M

Ollama
How to use QuantumAI-Blockchain/aether-v7.1-unified with Ollama:
```
ollama run hf.co/QuantumAI-Blockchain/aether-v7.1-unified:Q4_K_M
```

Unsloth Studio

How to use QuantumAI-Blockchain/aether-v7.1-unified with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantumAI-Blockchain/aether-v7.1-unified to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantumAI-Blockchain/aether-v7.1-unified to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantumAI-Blockchain/aether-v7.1-unified to start chatting

How to use QuantumAI-Blockchain/aether-v7.1-unified with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuantumAI-Blockchain/aether-v7.1-unified:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "QuantumAI-Blockchain/aether-v7.1-unified:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use QuantumAI-Blockchain/aether-v7.1-unified with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuantumAI-Blockchain/aether-v7.1-unified:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default QuantumAI-Blockchain/aether-v7.1-unified:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use QuantumAI-Blockchain/aether-v7.1-unified with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf QuantumAI-Blockchain/aether-v7.1-unified:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "QuantumAI-Blockchain/aether-v7.1-unified:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use QuantumAI-Blockchain/aether-v7.1-unified with Docker Model Runner:
```
docker model run hf.co/QuantumAI-Blockchain/aether-v7.1-unified:Q4_K_M
```

Lemonade

How to use QuantumAI-Blockchain/aether-v7.1-unified with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantumAI-Blockchain/aether-v7.1-unified:Q4_K_M

Run and chat with the model

lemonade run user.aether-v7.1-unified-Q4_K_M

List all available models

lemonade list

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).

Full model contents & on-chain verification (added 2026-07-16)

This repo now contains every byte needed to reconstruct the served, chain-attested model — not just the adapter. The Sephirot adapter is a routing MoE: unlike a LoRA it cannot be merged into dense weights (routing is input-dependent), so "the full model" is, exactly and honestly, these components plus the loader:

file	sha256	role
`qwen2.5-7b-instruct-q4_k_m.gguf`	`65b8fcd92af6b4fefa935c625d1ac27ea29dcb6ee14589c55a8f115ceaaa1423`	frozen 7B base (Qwen2.5-7B-Instruct, Q4_K_M, Apache-2.0)
`adapter_model.safetensors`	`564910ef462646a4789cdf4a31d4623cb50d1f3f1bf8374aa0129255df05cae7`	10-expert Sephirot MoE adapter (1.18M params)
`tokenizer.json`	`c0382117ea329cdf097041132f6d735924b697924d6f6fc3945713e96ce87539`	Qwen2 tokenizer
`adapter_config.json`	—	adapter shape/config

Verify against the chain: the Aether Mind computes a manifest root over (base, adapter, tokenizer, config) at load time — ModelManifest in aether-core/crates/aether-transformer/src/v7/manifest.rs (repo QuantumAI-Blockchain/qubitcoin-aether). The served model's root is

488b3387844c7bf087ae1b146457f4dcf5e809de204bc18728419d588217aaf2

which is the checkpoint recorded and quorum-finalized on QBC chain 3303 (continuous block 1,097,990, QbcModelRegistry round 1) and reported live by https://aether-gpu.qbc.network/aether/info. Download these files, run the manifest builder, and you get the same root the validators attested — the model you are holding is provably the model the chain tracks and the site serves.

Base-model attribution: Qwen2.5-7B-Instruct © Alibaba Cloud, Apache License 2.0. The GGUF here is the exact file the production mind loads (uploaded so the manifest is reproducible from this repo alone).

Downloads last month: -

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for QuantumAI-Blockchain/aether-v7.1-unified

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Quantized

(365)

this model