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This information was auto-extracted from the GitHub repository and may be incomplete or out of date.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.
LoRA + gradient checkpointing fits on 24GB consumer GPUs for sub-7B students. Full 33B bf16 training requires 4-8× H100/H200. Stage 3 on-policy distillation is compute-intensive; typical wall-time ~36 hours on single A100 for 5k OPD steps.
Validator runs split architecture: secure host (Hetzner) holds wallet keys and chain access; remote GPU pod (Lium) runs inference. Kimi K2.6 requires 8× H200 with tensor-parallel-size=8. B200 eval setup documented in docs/B200_EVAL_SETUP.md.
Install the distillation package and dependencies from source
pip install -e .
Run all 15 pre-submission validator checks locally to verify architecture, params, tokenizer, quantization, and format compliance
python test_miner.py --model-repo your-username/your-model
Full local eval including KL scoring (requires GPU); compare against current king with --king-repo flag(optional)
python check_model.py --model-repo your-username/your-model --eval
Dry-run submission (validates everything without committing on-chain); fix any validation errors before proceeding
python miner.py --network finney --netuid 97 --wallet-name my_wallet --hotkey-name my_hotkey --model-repo your-username/your-distilled-model --dry-run
Commit your model to the subnet (PERMANENT — interactive confirmation required; one commitment per hotkey, forever)
python miner.py --network finney --netuid 97 --wallet-name my_wallet --hotkey-name my_hotkey --model-repo your-username/your-distilled-model
Install the distillation package and dependencies (requires: bittensor, torch, transformers, vllm, fastapi, etc.)
pip install .
Register a validator hotkey on subnet 97 via the standard Bittensor flow
btcli subnet register --netuid 97
Run the validator via the wrapper script; or use PM2: pm2 start scripts/run_validator.sh --name distil-validator
bash scripts/run_validator.sh
Run the GPU eval pod remotely (Lium setup); vLLM teacher generation + student HF logit extraction; teacher logits GPU-resident, early stopping enabled
python scripts/pod_eval_vllm.py