# Into: EvolAI

Miners train language models and publish them to HuggingFace. Validators score them on-chain. Best model wins the rewards.

// Decentralizing the LLM benchmark.

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### What is EvolAI?

EvolAI (SN47) is a Bittensor subnet for competitive language model training. Miners train their own LLMs and publish them publicly on HuggingFace. Validators then evaluate those models and assign scores that determine reward distribution.

**The simple version:** It's like Kaggle for language models, but on a blockchain. Train a model, post it publicly, get scored by the network, earn rewards based on how your model performs.

**Centralized equivalent:** The Hugging Face Open LLM Leaderboard, or competitive benchmarking platforms like Kaggle.

**How it works:**
- **Miners** train language models using either a Transformer or Mamba2 architecture, publish them publicly on HuggingFace with "evolai" in the name, and register them on the subnet with a wallet and hotkey. Miners re-register each time they publish an updated model.
- **Validators** evaluate submitted models. The validator software requires a GPU with 80GB VRAM to run evaluations.

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### Why This Matters

- **The problem it solves:** Centralized LLM leaderboards are run by single teams. That creates a trust gap: who picks the benchmark tasks, who can game the eval? EvolAI moves that scoring process onto an incentivized validator network.
- **The opportunity:** Open model evaluation is still a largely unsolved problem. A neutral, economically-incentivized benchmark could matter as the open-source LLM ecosystem scales.
- **The Bittensor advantage:** Validators are staked participants with skin in the game. Dishonest scoring costs them. Miner rewards track directly to model quality, which creates continuous competitive pressure to improve.
- **Traction signals:** The subnet is early. GitHub shows 31 commits from a single contributor, with the most recent commit just two days ago. No official website, Discord, or Twitter has been published on-chain. Price has moved sharply positive, up 31% over 7 days and 29% over 30 days, with net TAO inflows over the past week positive at around 160 TAO.

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## Full Analysis

**Category:** Model Fine-Tuning | **Centralized Competitor:** Hugging Face Open LLM Leaderboard, Kaggle

The case for on-chain model evaluation is straightforward: leaderboards today require trusting the operator. EvolAI delegates that trust to an economically-incentivized validator network on Bittensor, where validators are financially penalized for inconsistency and rewarded for accurate scoring.

**Mechanism:**

According to the subnet's public repository, EvolAI runs two parallel tracks: `transformer` and `mamba2`. Miners choose a track, train a model, and upload it to HuggingFace as a public checkpoint. The model name must contain "evolai" to be eligible. Once uploaded, miners register their model on the subnet by specifying their wallet, hotkey, and track. They then receive a challenge from a validator to complete registration. After each new model version is published, miners re-register. Validators fetch the submitted models and run evaluations, requiring 80GB of GPU VRAM to do so.

The subnet's name and framing, "evolving AI systems," points to a design intent around iterative improvement: miners are pushed by the reward structure to continuously update and improve their models rather than coasting on a one-time submission.

EvolAI is thin and early. The root-side pool holds around 1,189 TAO, with a root proportion of 50.5%, meaning roughly half the pool depth comes from protocol subsidy rather than organic demand. Price discovery is still in progress. The subnet holds 0.63% of total network emissions. Daily buy volume has been around 421 TAO against sell volume of ~365 TAO, giving a positive net on 24-hour flow. Chain buys sit at 0.47% of emissions, a separate metric from the emission share itself and not a risk indicator at this level.

The single GitHub contributor and 31-commit codebase reflect very early stage development. The last commit was two days ago, suggesting active work. No website, Discord, or Twitter handle appeared in the on-chain identity, which limits community discovery at this point.

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### Risk Factors

- **Thin liquidity:** The root-side pool holds around 1,189 TAO. Slippage on entry or exit will be material, and a single large position can move the alpha price significantly.
- **Development concentration:** One contributor on GitHub means key-person risk. If the developer steps back, development stalls with no visible backup.
- **No public community presence:** No official website, Discord, or social accounts have been registered on-chain. Discovery is limited to on-chain data and secondary aggregators.
- **Early pool, high root proportion:** Root prop at 50.5% means the pool is still in early price discovery and has not yet established deep organic demand. Price action here can be volatile relative to larger subnets.
- **Undocumented evaluation methodology:** The public repository describes how to submit and register models, but does not detail the specific evaluation criteria or benchmark tasks validators use. This opacity is a risk for miners trying to optimize their training and for anyone assessing the system's integrity.

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Another subnet, unpacked.

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