docs(readme): fix accidental wrong-README upload — restore model card with GPU hardware + naming corrections

7a26d20 verified 3 days ago

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base_model: Qwen/Qwen2.5-7B-Instruct
library_name: peft
license: apache-2.0
tags:
  - lora
  - peft
  - qubitcoin
  - aether
  - blockchain
  - quantum
language:
  - en
pipeline_tag: text-generation
model-index:
  - name: aether-v5.2-lora
    results:
      - task:
          type: text-generation
          name: MMLU
        dataset:
          name: MMLU
          type: cais/mmlu
        metrics:
          - type: accuracy
            value: 0.6939
            name: accuracy
      - task:
          type: text-generation
          name: ARC-Challenge
        dataset:
          name: ARC-Challenge
          type: ai2_arc
        metrics:
          - type: accuracy
            value: 0.5392
            name: accuracy
          - type: accuracy_norm
            value: 0.57
            name: accuracy_norm
      - task:
          type: text-generation
          name: ARC-Easy
        dataset:
          name: ARC-Easy
          type: ai2_arc
        metrics:
          - type: accuracy
            value: 0.8194
            name: accuracy
      - task:
          type: text-generation
          name: HellaSwag
        dataset:
          name: HellaSwag
          type: hellaswag
        metrics:
          - type: accuracy
            value: 0.5888
            name: accuracy
          - type: accuracy_norm
            value: 0.7769
            name: accuracy_norm
      - task:
          type: text-generation
          name: TruthfulQA
        dataset:
          name: TruthfulQA-MC2
          type: truthful_qa
        metrics:
          - type: accuracy
            value: 0.5707
            name: accuracy

Aether v5.2 LoRA — QuantumAI Blockchain Domain Adapter

A LoRA fine-tune of Qwen/Qwen2.5-7B-Instruct on the Aether curated corpus — text grounded in the QuantumAI Blockchain (which issues the Qubitcoin / QBC currency), quantum + AI research, and adjacent domains the Aether Mind on-chain knowledge system specializes in.

This is the v5.2 release of the Aether adapter line, the most recent public checkpoint at time of publish.

What you're getting

Field	Value
Base model	`Qwen/Qwen2.5-7B-Instruct`
Adapter type	LoRA via 🤗 PEFT
Rank (`r`)	16
Alpha	32
Dropout	0.05
Trainable params	~1% of base
Sequence length	2048
Training corpus	`aether-curated-v3.jsonl` — Aether-curated knowledge mixture (~165 MB; ~10⁵ examples)
Checkpoint published	step 3200 (the checkpoint that produced the evaluated numbers below)
License	Apache-2.0 (matches base)

Evaluation

Run via lm-evaluation-harness on the merged adapter (base + LoRA), against the Qwen/Qwen2.5-7B-Instruct base and the prior aether-v5.1.1 adapter for delta comparison.

Benchmark	aether-v5.1.1	aether-v5.2	Δ vs v5.1.1
MMLU	0.6950	0.6939	flat
ARC-Easy	0.7348	0.8194	+8.5 pp
ARC-Challenge	0.4420	0.5392	+9.7 pp
ARC-Challenge (norm)	0.4701	0.5700	+10.0 pp
HellaSwag	0.5896	0.5888	flat
HellaSwag (norm)	0.7788	0.7769	flat
TruthfulQA-MC2	0.5161	0.5707	+5.5 pp

Honest summary

Real gains on the reasoning + factual-honesty benchmarks (ARC-Easy, ARC-Challenge, TruthfulQA). ARC-Challenge in particular jumps nearly 10 points normalized — that's the closest of these benchmarks to the kind of grounded reasoning the Aether corpus actually trains on.
Flat on MMLU + HellaSwag. The base is already strong on general knowledge + commonsense; this LoRA wasn't designed to shift them, and didn't.
No regressions.

Intended uses

This adapter is intended for:

On-chain Aether research. Generating reasoning traces against the QuantumAI Blockchain / Aether knowledge graph for Proof-of-Thought attestation. The model has the protocol context required to answer questions about Substrate pallets, VQE mining, the Sephirot cognitive architecture, HMS-Phi, and the wider chain ecosystem.
Domain Q&A. Quantum computing fundamentals, post-quantum cryptography (Dilithium, ML-KEM), and the specific design choices of the QuantumAI Blockchain.
Distillation upstream. Generate teacher outputs for the smaller on-chain Aether (a Qwen2.5-0.5B variant) to learn from.
General reasoning with a modest bias toward step-by-step chains-of-thought, where the ARC-Challenge gain translates.

Out-of-scope uses

Safety-critical decisions. No red-team eval was performed.
Financial / legal advice. This is a knowledge-domain adapter; it has no training data designed to make it a financial or legal advisor.
Code generation in production. No code-eval benchmark was run. Treat any generated code as draft until you've reviewed it.
Production deployment without your own evaluation. TruthfulQA alone is a thin safety signal.

Bias, risks, and limitations

The base model (Qwen/Qwen2.5-7B-Instruct) inherits Qwen's known biases — see the upstream model card. The LoRA adapter:

Amplifies the QuantumAI Blockchain worldview. The training data is intentionally curated around the chain's design choices (golden- ratio economics, SUSY-inspired consensus framing, the Sephirot cognitive overlay). Prompts that invite the model to compare QBC / the chain against alternatives will lean toward the curated narrative. This is by design — disclose if you re-publish in a comparison context.
Does not improve safety. TruthfulQA went up 5.5pp but that's one metric; we have not measured refusal rates, jailbreak resistance, or political-belief bias delta.
The configured 2-epoch run was cut to ~step 3080–3200 by host availability (out of 4406 configured). A complete 2-epoch run would plausibly show larger gains; this checkpoint is the longest contiguous training we have.

How to use

Load with PEFT on top of the base model:

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-7B-Instruct",
    torch_dtype="auto",
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")
model = PeftModel.from_pretrained(base, "QuantumAI-Blockchain/aether-v5.2-lora")

messages = [{"role": "user", "content": "Explain Proof-of-SUSY-Alignment in one paragraph."}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=256, do_sample=True, temperature=0.7)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

Or merge the adapter into a single artifact for faster inference:

merged = model.merge_and_unload()
merged.save_pretrained("./aether-v5.2-merged")

Training details

Hardware: NVIDIA RTX 3080 Ti (12 GB), 4-bit quantization (bnb-NF4), bf16 mixed precision.
Trainer: Axolotl wrapping 🤗 transformers / PEFT.
Optimizer: paged_adamw_8bit (bitsandbytes paged optimizer, low VRAM footprint).
Schedule: linear warmup 100 steps → cosine decay.
Learning rate: 1.0e-4.
Micro batch: 1, gradient accumulation: 8.
Epochs configured: 2 (training stopped at step 3200 — see "What didn't happen" below).

Carbon emissions

Trained on a single NVIDIA RTX 3080 Ti (consumer GPU, ~300 W TDP). We did not run a CodeCarbon tracker, so emissions are not measured precisely — but as a rough upper bound: ~350 W draw under load × ~13 hours wall clock (the step-3080 contiguous run) ≈ 4.5 kWh, low single-digit kg CO₂e on a grid mix. An H100 run of the same 2-epoch config would be faster but not dramatically lower energy per token.

Training data

aether-curated-v3.jsonl (~165 MB, ~10⁵ examples) is the Aether team's curated knowledge mixture: documentation, technical writing, reasoning traces, and protocol-specific corpora related to:

The QuantumAI Blockchain (Substrate, VQE mining, Proof-of-SUSY-Alignment, post-quantum signatures).
The Aether Mind on-chain neural cognitive engine (10 Sephirot attention domains, HMS-Phi, Proof-of-Thought).
Quantum computing fundamentals (VQE, Hamiltonian generation, qubit ansatze).
Adjacent CS / math reasoning content for transfer.

The dataset is not currently public — it is a curated mixture from many sources and has not been release-cleared at the per-source level. The model is the only public artifact in this line for now.

What didn't happen (honest caveats)

Training stopped early. Configured for 2 epochs (4406 steps); reached step 3080–3200 (~70%) before host availability cut the run short. The run was on a single consumer GPU (RTX 3080 Ti), 4-bit quantized, with paged_adamw_8bit to fit a 7B model in 12 GB VRAM. The numbers above are from the longest contiguous training run we have; a complete 2-epoch run would plausibly show larger gains.
No instruction-following or safety eval beyond TruthfulQA-MC2. No red-team eval. No bias audit. No code-generation benchmark. Don't recommend this for production safety-critical use without your own evals.
LoRA only, not merged. This release ships the adapter weights (adapter_model.safetensors). Merge into the base yourself for faster inference, or use directly via PEFT.

Connection to the QuantumAI Blockchain

The Aether Mind is a Rust neural cognitive engine that runs on the QuantumAI Blockchain — every block records attention-derived consciousness metrics (HMS-Phi) and Proof-of-Thought hashes on-chain via the pallet_qbc_aether_anchor pallet. The same chain hosts an 8-qubit VQE mining consensus (Proof-of-SUSY-Alignment), a QVM-compatible smart contract layer with 10 quantum opcodes, and post-quantum signatures (CRYSTALS-Dilithium5 + ML-KEM-768 P2P).

The on-chain Aether Mind binary uses a different, smaller transformer for live inference (a Qwen2.5-0.5B variant optimized for ~2.4 GB RAM with the 10-Sephirot attention overlay). This v5.2 adapter on Qwen2.5-7B is the larger off-chain Aether — used for batch reasoning workloads and as an upstream model the on-chain variant can distil from.

License + citation

Apache-2.0 (matches the base model license).

@misc{aether_v52_lora_2026,
  title  = {Aether v5.2 LoRA --- QuantumAI Blockchain Domain Adapter},
  author = {{BlockArtica} and {QuantumAI-Blockchain}},
  year   = {2026},
  url    = {https://huggingface.co/QuantumAI-Blockchain/aether-v5.2-lora},
}

QuantumAI-Blockchain
/

aether-v5.2-lora