v0.1.0 — first verified proof-of-training baseline

The first complete, working release of the SparkDistill miner economy: train a Triton-specialized student on verified data, prove the run cryptographically, and verify it from public artifacts alone.

Highlights

Two-track miner economy

• Dataset track (dataset:xs–xl): SparkProof bundles on Blackwell CC VMs, verified end-to-end by CI (release gate, GPU CC attestation, sha256 pinning, novelty), auto-merged registry PRs, and automatic aggregation into the canonical mining dataset (gittensor-model-hub/sparkproof-mining).
• Training track (eval:BASELINE, eval:XS–XL): public Axolotl recipes trained on registry-backed data, scored against the canonical frontier.

Weights-free, claim-bound proof of training

• Proof bundles carry the claim, not the weights: eval scores, training claims, and a per-file sha256 manifest of the checkpoint (~12KB instead of ~8.8GB).
• The bundle's claim_sha256 is bound into the NRAS-signed GPU CC attestation as the nonce — a passed attestation cannot be reused for a different claim.
• eval.verify runs the whole validator side in one command: attestation, hardware corroboration (RTX PRO 6000 / GB20X), 5-hour training budget, mix provenance, claim binding, checkpoint hash comparison, and held-out score re-runs against a locally reproduced checkpoint. First run on a student/phase gets eval:BASELINE mechanically.

Triton domain evaluation

• Vendored, version-pinned TritonBench harness (Triton 3.7.1, Blackwell SM12x/SM10x): generated kernels are compiled and executed on the GPU, correctness requires an executed reference comparison, and problem required_patterns are enforced.
• Deterministic serving for comparable claims: pinned vLLM (scripts/install_serve.sh) and greedy decoding.
• General basket (GSM8K, BFCL, HumanEval, IFEval, MMLU-Pro, AIME, GPQA-Diamond via lm-eval) as the regression guard.

First verified baseline on the ledger

• Run 2026-07-11-qwen3.5-4b-mining-001: Qwen3.5-4B LoRA on the canonical mining dataset, trained in 97s on a Targon RTX PRO 6000 Blackwell CC node, attested (nonce-bound), published, and verified — triton 0.4278 (syntax 100%, exec 0%) / gsm8k 0.6.
• runs/frontier.json seeds the score every next submission must beat.

Tooling & docs

• Reproducible training on Blackwell (recipe prep, FA2/FA3 selection, small-dataset guards), HF→local dataset export, miner guide, contribution template, and the project site at https://gittensor-model-hub.github.io/SparkDistill/

The frontier to beat

triton 0.4278 — with 0% kernel execution pass, the mining opportunity is clear: more verified Triton training data.