Bittensor mining.

✳ Training & evaluation

Train. Submit. Evaluate. Reward.

1. 01

   Train

   Miners train compatible adapters and decision heads with a fixed one-epoch recipe and different seeds.

2. 02

   Submit

   Each miner freezes a candidate and signs its checkpoint hash for the validator.

3. 03

   Evaluate

   The validator verifies the checkpoint and runs its own evaluation of the model’s probabilities.

4. 04

   Reward

   Family-macro Brier skill determines proposed weights across eligible candidates.

The local loop works on Apple Silicon and an RTX 4090. Accuracy and latency are diagnostics, not separate reward components. The subnet’s family-macro Brier and JevBench Brier use different aggregation rules.

✳ Participants

Three miners. One validator. One host.

Three registered miners and one validator completed the recorded subnet 579 rehearsal.

Miners · UIDs 1–3

Trained and submitted three distinct checkpoints

RECORDED PARTICIPATION

Validator · UID 0

Evaluated the checkpoints and published weights

RECORDED PARTICIPATION

One operator ran all four processes on one host. Services exited after the round; these counts do not indicate current availability or independent operators.

✳ Testnet publication

Bittensor testnet · Subnet 579

Each 0.8B checkpoint trained for one epoch on 224 examples, calibrated on 112 questions, and was evaluated on the same 224 test questions. This reused synthetic development benchmark is separate from the public JevBench comparison. All compute ran on one Apple M4 Pro with 24 GiB memory, with GPU jobs serialized.

This closed rehearsal demonstrates the training-to-chain path. It does not establish mainnet deployment, miner earnings, open competition, or automatic model promotion.