Quasar is Bittensor subnet 24 (SN24), a project in the Model Fine-Tuning lane. One king, one-hot weights.
A king-of-the-hill subnet for small open language models. Miners train a Quasar 3B mixture-of-experts model, publish the pinned weights to Hugging Face, and commit the revision on-chain. Validators run paired-KL duels and a multi-axis composite evaluator, then hand all weight to the single best model standing.
It is an educational deep dive, not a recommendation to stake. For live price, pool depth, and performance, always open the live subnet page.
What is Quasar (SN24)?
Quasar is a Bittensor subnet run by SILX Labs that turns small-model training into a continuous, on-chain tournament. Miners submit their own fine-tunes of the Quasar 3B foundation model as public Hugging Face repositories. Validators evaluate every valid submission and the strongest model wins the crown, which collects the subnet's full validator weight until something better arrives.
In plain English: It is a leaderboard you cannot game. Miners train models and pin them publicly. Validators run the same tests on every entry. The current best model is the king. Beat the king and you become the king.
Centralized reference points: Open LLM Leaderboard, LMSYS Arena, Hugging Face model hub competitions.
How miners and validators work
Like every Bittensor subnet, SN24 is a two-sided market: miners produce work, validators score it, and emissions follow those weights under the network’s dTAO economics.
- Miners train Quasar-compatible models within a fixed parameter cap, host them as pinned public Hugging Face revisions, and submit one repo per registered hotkey.
- Validators run pre-checks (architecture, tokenizer, safetensors, dedup), score each model with paired-KL duels plus a composite evaluator covering capability, conversational quality, generation discipline, and robustness, then assign one-hot weights to the current king.
Mechanism and design
Miners build on a fixed base. The canonical architecture is defined by silx-ai/Quasar-3B-A1B-Preview, with vocab_size 248320 and a parameter cap enforced by validators. Submissions must be loadable with trust_remote_code, must avoid quantized formats (GPTQ, AWQ, GGUF, FP8), and must remain unchanged after the committed revision. Identical weight reuse is disqualified as COPY, deleted or modified repos as REMOVED.
Evaluation is two-layered. Validators run a composite evaluator that scores distribution match against a teacher (default Qwen/Qwen3.5-4B), capability axes (math, code, reasoning, instruction following, tool use, long context, robustness), conversational quality, and generation discipline that penalizes models which ramble, loop, or never answer. Prompts are block-seeded and rewritten so miners cannot overfit to a static answer key. The composite produces both a worst-axis score and a weighted aggregate.
The second layer is paired-KL duels between the challenger and the current king. Dethroning requires both a valid paired-KL win and a composite quality pass. Composite alone cannot override a direct head-to-head regression, and KL alone cannot crown a challenger. The winner of that two-test gate gets validator weight 1.0 on-chain. Everyone else gets 0.0, with a no-winner fallback UID configured so vTrust stays aligned when no submission passes safely.
Onchain reality matches the design at the validator side and complicates the picture at the market side. The repo has had a push every day or two through early June 2026, with recent commits stabilizing the crown quality gate, the validator round guards, and a 90/10 default safety split. Active miner count from TaoSwap reads 1, which is consistent with a king-of-the-hill design where one model holds the throne and validators set one-hot weights to that single UID rather than spreading weight across many active workers.…
Why the project exists
- The problem it solves: Open-model benchmarks reward a single submission once, then stop. Quasar reruns the contest forever, with cryptographically pinned entries and live incentives, so improvements show up immediately in chain rewards.
- The opportunity: Small-model quality is one of the most contested fronts in AI. A 3B model that can credibly hold its ground on reasoning, code, instruction following, and long context is commercially useful and cheap to serve.
- The Bittensor advantage: The crown gets the entire validator weight, so the incentive to dethrone is direct and unambiguous. There is no gradient of partial credit to coast on.
- Traction signals: 78 commits since the late-April 2026 rewrite, daily pushes from the lead engineer, and a published base checkpoint at silx-ai/Quasar-3B-A1B-Preview. The team also ships a separate live dashboard for the king and the chain-revealed weight target.
What stakers should check before buying alpha
Subnet storytelling is cheap. On-chain and product checks are not. Use the same framework as our subnet metrics guide:
| Check | Why |
|---|---|
| Real product / docs / repo | Separates shipping teams from narrative-only slots |
| Pool depth (TAO) | Your exit door: thin pools amplify slippage |
| Net flows (7d / 30d) | Under flow-based emissions, capital votes with its feet |
| Emission share | Whether the market is funding this subnet right now |
| Ownership concentration | Large holders can move a small alpha market |
| Immunity / deregistration | Post-immunity weak slots can lose the race (see immunity guide) |
Pull the live numbers on Quasar · SN24 rather than trusting any static screenshot in a blog post.
Risk factors
- Bus factor: Two GitHub contributors, with ahmed3520 doing essentially all of the recent work. A single key engineer carries the validator codebase.
- Outflows under a 0% emission share: Taoflow gives zero emissions to subnets running negative net flows. A 7-day outflow near 787 TAO keeps the subnet in that bucket until staking flows turn positive.
- Mechanism continuity: The current design is a fresh build on a re-registered netuid with a new owner address. Buyers comparing this Quasar to the version that traded earlier in the year are looking at a different team and a different product.
- One-hot weight risk: King-of-the-hill means a single model captures all validator weight. A buggy crown, a misconfigured threshold, or a flawed evaluation pass can concentrate emissions on the wrong model until the next round corrects it.
- Competition: Small-model leaderboards are crowded, and centralized venues have far more eyes on them. Quasar needs the cryptographic-commit angle and the incentive sharpness to translate into actual model quality that someone wants to run.
How this fits a TAO portfolio
Many holders keep a root vs subnet split: root for calmer TAO exposure, subnets like Quasar for thematic upside (and higher variance). If you use TaoScale, you can size SN24 from your wallet without handing over custody. See non-custodial staking.
- Live market: SN24 on TaoScale
- Browse and stake: /invest
- More subnet explainers: /blog/subnets
FAQ
What does Quasar do on Bittensor?
It is a leaderboard you cannot game. Miners train models and pin them publicly. Validators run the same tests on every entry. The current best model is the king. Beat the king and you become the king.
What is the SN24 alpha token?
When you stake TAO into subnet 24, you receive that subnet’s alpha. Its price versus TAO moves with the subnet’s pool (see what is dTAO).
How do I research Quasar before staking?
Read the mechanism above, open the official repo/docs when available, then verify pool depth, flows, and activity on SN24. Cross-check with the metrics checklist.
Is this financial advice?
No. TaoScale publishes educational explainers and live tools. Decisions to stake are yours alone.
Not financial advice. Educational overview of Quasar (SN24) compiled from public project materials and ecosystem documentation. Mechanisms and market structure change, so verify live data and official sources before acting.
