Mine open reasoning.
Affine Discord | Live Dashboard
Affine is an incentivized RL environment that pays miners who make incremental improvements on a set of tasks (such as program abduction or coding). The mechanism is sybil-proof, decoy-proof, copy-proof, and overfitting-proof.
How does Affine work?
Affine validators incentivize miners to submit models on Bittensor. Miners commit a HuggingFace (model, revision) pair on chain; validators host the inference (currently via Targon, optionally an operator-managed B300 fleet). The validator-side scheduler walks the queue of pending miners in first_block order — each one faces the current champion across every evaluation environment in a single back-to-back contest. The challenger only dethrones the champion when they win strictly across all environments by a per-env margin; otherwise they're permanently terminated and the queue advances to the next miner. Every ~7200 blocks (~24h) the per-env task-id pool is refreshed and the (current) champion is re-sampled before the queue continues. The winner-takes-all weight goes to the champion until they're dethroned.
Why Affine?
Directed incentives for RL have never been achieved. The ability to direct intelligence and aggregate the work effort of a large, non-permissioned group of individuals on RL tasks will unlock rapid advancement in intelligence. We intend to commoditize reasoning (intelligence's highest form) and break the intelligence sound barrier.
# Install uv package manager
curl -LsSf https://astral.sh/uv/install.sh | sh
# Clone and install Affine
git clone https://github.com/AffineFoundation/affine.git
cd affine
uv venv && source .venv/bin/activate && uv pip install -e .
# Verify installation
afAffine uses Affinetes for container orchestration, providing:
- Clean, lightweight container management
- Support for local and remote Docker deployments
- Environment caching for improved performance
- Type-safe environment definitions
All evaluation environments are packaged as pre-built Docker images, eliminating the need for complex sandbox management.
Learn how to:
- Set up your environment and configure API keys
- Pull models from the network
- Improve models with reinforcement learning
- Upload to HuggingFace and commit on-chain (validator hosts inference)
- Use CLI commands to query public rank/status and miner metadata
Learn how to:
- Set up and configure your validator
- Run with Docker (recommended) or locally
- Monitor validator performance
- Troubleshoot common issues
- Set weights on-chain
- 📚 FAQ - Frequently asked questions
Affine can be used as an SDK for evaluating models across different environments.
Examples:
examples/sdk.py- Evaluate miners from the network on DED-V2 and ABD-V2 environmentsexamples/sdk2.py- Evaluate custom models by specifying model parameters directly
Key Features:
- Evaluate registered miners by UID
- Evaluate custom models with direct parameters (model, base_url, temperature)
- Support for multiple environments (DED-V2, ABD-V2, etc.)
- List all available environments
- Async API for efficient batch evaluation
See the example files for complete usage patterns.
- Discord: Join our community
- Dashboard: https://www.affine.io/
- GitHub: https://github.com/AffineFoundation/affine-cortex
- FAQ: docs/FAQ.md