Neuron registration trends
The alpha price in τ, day by day, and what moved it each way.
Log change over the last 30 days −14.14%: protocol buys +7.60%, root sells −0.71%, users net −18.95%, cross-subnet −2.07%, unattributed +0.0002%.
The chart follows the alpha price in τ. Each period starts at its opening price and steps through the mechanisms below, in a fixed order, to its closing price: a step that rises pushed the price up, one that falls pushed it down, and its height is how far it moved the price. A thin line carries each close into the next period’s open; it is dashed where the two differ. The faint candle behind each period is the market’s own open, high, low and close, for context.
With many periods on screen (90 days, or hours), the steps would be too thin to read, so the chart draws the price as a line instead and colours each period by the mechanism that moved it most. Hover a period for the full step-by-step breakdown.
Not here, because they do not move the spot price: the per-block TAO injection into the pool, and burns.
All figures are log changes, not simple percentage changes, so the parts add up exactly to the total; on big moves the two differ (a 10% log fall is a 9.5% price fall).
2026-11-06 UTC, as of ≈23:50 UTC (block 9,450,443)
The day isn’t over yet, so these figures keep changing. They are in no daily total, average or rank.
Language model development requires continuous evaluation against baseline performance, but centralized benchmark systems create bottlenecks, limited hardware capacity, and opaque evaluation criteria. Organizations need transparent, scalable mechanisms to benchmark new model checkpoints against established baselines without relying on single evaluation endpoints.
A decentralized network of GPU validators can evaluate submissions continuously, applying consistent masked-document scoring protocols and publishing results transparently. This approach removes centralization bottlenecks, distributes computational load, and creates verifiable audit trails for evaluation decisions.
Teutonic implements this on Bittensor Subnet 3 by operating a king-of-the-hill competition where miners submit immutable model checkpoints encrypted to Ed25519 hotkeys. A validator orchestrates paired cross-entropy evaluation against the current champion, applies document-boundary masking to isolate meaningful evaluation windows, and uses token-weighted bootstrap resampling for robust scoring. Winners become the new reigning model; results and competition state are published for verification.
Auto-generated from the GitHub repository and may not reflect the latest changes.
Teutonic is a king-of-the-hill pretraining system for Bittensor subnet 3 where miners submit model checkpoints that are evaluated through paired cross-entropy scoring against the current champion. A validator verifies submissions, orchestrates GPU-based evaluation with document-masked scoring, and updates subnet weights based on performance metrics that support multiple competition tracks.
Miners submit immutable model checkpoints encrypted to Ed25519 hotkeys. The validator receives submissions via public mailbox, verifies authenticity, and dispatches paired evaluation jobs (challenger vs current king) to remote GPU evaluators. Evaluators perform document-masked cross-entropy scoring with bootstrap resampling.
The validator collects results, determines the winner through token-weighted metrics, updates subnet weights accordingly, and publishes state including current king and competitions metadata.
Evaluation isolates EOS-delimited document fragments within sampled windows, resets their positions, and excludes predictions across document boundaries. Scores are weighted by scored-token counts with bootstrap resampling of paired original windows. Optional long-document evaluation samples complete documents between 2049-8192 tokens with category-proportional coverage, supporting MAIN, MATH, CODE and TEXT categories with combined token-weighted verdicts.
The sections above were auto-generated from the GitHub repository and may not reflect the latest changes. View repository (github.com, opens in a new tab, external site)
Coordinated Learning
This information was auto-extracted from the GitHub repository and may be incomplete or out of date. Always refer to the subnet's GitHub repository (github.com, opens in a new tab, external site) for the latest instructions.
Hardware requirements: see the subnet's repository (github.com, opens in a new tab, external site)
Clone the repository
git clone https://github.com/unarbos/teutonic.git && cd teutonic
Create and activate Python 3.11+ virtual environment
python3 -m venv .venv && source .venv/bin/activate
Install miner dependencies (bittensor, boto3, httpx, PyNaCl)
python -m pip install -e '.[miner]'
Configure wallet path for common usage
teutonic-miner configure --wallet-path "$WALLET_PATH"
Create Ed25519 hotkey (required for encryption)(optional)
btcli wallet new-hotkey --wallet-name "$WALLET_NAME" --hotkey "$HOTKEY_NAME" --wallet-path "$WALLET_PATH" --crypto-type ed25519
Register hotkey on subnet and activate mailbox (may require TAO)
teutonic-miner register --wallet-name "$WALLET_NAME" --hotkey-name "$HOTKEY_NAME" --network "$NETWORK" --netuid "$NETUID"
Retrieve encrypted temporary R2 upload credentials
teutonic-miner auth --hotkey "$HOTKEY_NAME"
Upload model checkpoint to R2
teutonic-miner upload --hotkey "$HOTKEY_NAME" --name "$MODEL_NAME" "$MODEL_DIR"
Finalize and submit model (irreversible)
teutonic-miner ready --hotkey "$HOTKEY_NAME"
| Day (UTC) | Share |
|---|---|
| 2026-11-05 | 2.94% |
| 2026-11-04 | 2.94% |
| 2026-11-03 | 2.95% |
| 2026-11-02 | 2.90% |
| 2026-11-01 | 2.89% |
| 2026-10-31 | 2.91% |
| 2026-10-30 | 2.82% |
| 2026-10-29 | 2.94% |
| 2026-10-28 | 2.93% |
| 2026-10-27 | 2.89% |
| 2026-10-26 | 2.88% |
| 2026-10-25 | 2.89% |
| 2026-10-24 | 2.87% |
| 2026-10-23 | 2.84% |
| 2026-10-22 | 2.82% |
| 2026-10-21 | 2.80% |
| 2026-10-20 | 2.82% |
| 2026-10-19 | 2.83% |
| 2026-10-18 | 2.92% |
| 2026-10-17 | 2.99% |
| 2026-10-16 | 3.03% |
| 2026-10-15 | 3.13% |
| 2026-10-14 | 3.17% |
| 2026-10-13 | 3.24% |
| 2026-10-12 | 3.24% |
| 2026-10-11 | 3.23% |
| 2026-10-10 | 3.25% |
| 2026-10-09 | 3.27% |
| 2026-10-08 | 3.28% |
| 2026-10-07 | 3.31% |
| 2026-10-06 | 3.32% |
| 2026-10-05 | 3.33% |
| 2026-10-04 | 3.32% |
| 2026-10-03 | 3.41% |
| 2026-10-02 | 3.58% |
| 2026-10-01 | 3.65% |
| 2026-09-30 | 3.90% |
| 2026-09-29 | 3.84% |
| 2026-09-28 | 3.89% |
| 2026-09-27 | 3.93% |
| 2026-09-26 | 4.12% |
| 2026-09-25 | 4.16% |
| 2026-09-24 | 4.14% |
| 2026-09-23 | 4.20% |
| 2026-09-22 | 4.36% |
| 2026-09-21 | 4.34% |
| 2026-09-20 | 4.32% |
| 2026-09-19 | 4.34% |
| 2026-09-18 | 4.09% |
| 2026-09-17 | 3.96% |
| 2026-09-16 | 3.93% |
| 2026-09-15 | 3.97% |
| 2026-09-14 | 4.10% |
| 2026-09-13 | 4.06% |
| 2026-09-12 | 4.01% |
| 2026-09-11 | 3.90% |
| 2026-09-10 | 3.81% |
| 2026-09-09 | 4.05% |
| 2026-09-08 | 4.63% |
| 2026-09-07 | 4.70% |
| 2026-09-06 | 4.64% |
| 2026-09-05 | 4.65% |
| 2026-09-04 | 4.67% |
| 2026-09-03 | 4.69% |
| 2026-09-02 | 4.35% |
| 2026-09-01 | 3.69% |
| 2026-08-31 | 3.78% |
| 2026-08-30 | 3.87% |
| 2026-08-29 | 3.95% |
| 2026-08-28 | 4.01% |
| 2026-08-27 | 3.72% |
| 2026-08-26 | 3.82% |
| 2026-08-25 | 3.91% |
| 2026-08-24 | 3.75% |
| 2026-08-23 | 3.69% |
| 2026-08-22 | 3.63% |
| 2026-08-21 | 3.66% |
| 2026-08-20 | 3.69% |
| 2026-08-19 | 3.71% |
| 2026-08-18 | 3.99% |
| 2026-08-17 | 4.30% |
| 2026-08-16 | 4.39% |
| 2026-08-15 | 4.05% |
| 2026-08-14 | 3.45% |
| 2026-08-13 | 3.42% |
| 2026-08-12 | 3.49% |
| 2026-08-11 | 3.62% |
| 2026-08-10 | 3.70% |
| 2026-08-09 | 3.69% |
| 2026-08-08 | 3.73% |
