Loading
Loading

The same model, small enough to run on your phone. Compress the frontier models into GGUFs.
This information was auto-extracted from the GitHub repository and may be incomplete or out of date. Always refer to the subnet's GitHub repository for the latest instructions.
Compression method is miner-chosen; GPU needed only for the compression development process, not for submission. Compressed artifact can be served from any accessible URI.
Scoring validators run three judge models (SmolLM2-1.7B, Phi-3-mini-4k, OLMo-2-1124-7B) in parallel. CPU validators can run audits at L0/L1 levels without GPU.
Clone the Ralph repository
git clone https://github.com/RalphLabsAI/ralph && cd ralph
Install core dependencies and chain SDK for on-chain operations
pip install -r requirements.txt -r requirements-chain.txt
Verify your compressed model meets the bit-budget requirement before committing
python -m eval.bitrate path/to/your/model.gguf
Dry-run the commit to see what will be written on-chain(optional)
python -m miner.submit commit --ckpt ./my-compressed-qwen3 --tier ternary --uri hf://<you>/<repo>@<commit-sha> --wallet <wallet> --hotkey <hotkey> --netuid 40 --dry-run
Seal the hash of your exact bytes on-chain before the round nonce exists
python -m miner.submit commit --ckpt ./my-compressed-qwen3 --tier ternary --uri hf://<you>/<repo>@<commit-sha> --wallet <wallet> --hotkey <hotkey> --netuid 40
Publish the artifact URI and salt after the round opens
python -m miner.submit reveal --ckpt ./my-compressed-qwen3 --wallet <wallet> --hotkey <hotkey> --netuid 40
Clone the Ralph repository
git clone https://github.com/RalphLabsAI/ralph && cd ralph
Install core dependencies and chain SDK
pip install -r requirements.txt -r requirements-chain.txt
Install GPU-specific dependencies for model scoring (L2/L3 audits)(optional)
pip install -r requirements-gpu.txt
Install system package for code execution sandbox
sudo apt-get install bubblewrap
Run a single audit pass at L0 (arithmetic verification, CPU-only)(optional)
python -m eval.auditor --once --require L0 --signer <validator-record-key>
Continuously audit new rounds at L0 and L1 (exam selection verification)(optional)
python -m eval.auditor --follow --require L0,L1 --signer <key> --interval 600
Audit with judge model inference (L2), requires GPU(optional)
python -m eval.auditor --follow --require L0,L1,L2 --signer <key> --observer <judge-hf-id>
Run as weight-setting validator: verify and set emission weights on-chain(optional)
python -m eval.auditor --follow --signer <key> --anchor-hotkey <scoring-validator-ss58> --wallet <yours> --hotkey <yours> --interval 1200 --set-weights