Note: The following documentation assumes you are familiar with basic Bittensor concepts: Miners, Validators, and incentives. If you need a primer, please check out https://docs.bittensor.com/learn/bittensor-building-blocks.
The Finetuning subnet 37 rewards miners for fine-tuning Large Language Models (LLMs). At launch the first competition is evaluated with data generated from a continuous stream of synthetic data provided by subnet 18. It is a continuous fine-tuning benchmark, with new data generated daily.
The mechanism works like this:
1. Miners train and periodically publish models to 🤗 Hugging Face and commit the metadata for that model to the Bittensor chain to sign up for a specific competition and prove the time of training.
2. Validators download the models from 🤗 Hugging Face for each miner based on the Bittensor chain metadata and continuously evaluate them against the synthetic data. For each competition, only the top model will receive nonzero weights. They also log results to [wandb](https://wandb.ai/opentensor-dev/finetuning).
3. The Bittensor chain aggregates weights from all active validators using Yuma Consensus to determine the proportion of TAO emission rewarded to miners and validators.
See the Miner and Validator docs for more information about how they work, as well as setup instructions.
Bittensor hosts multiple incentive mechanism through which miners are evaluated by validators for performing actions well. Validators perform the process of evaluation and 'set weights', which are transactions into Bittensor's blockchain. Each incentive mechanism in Bittensor is called a 'subnet'. Weights and the amount of TAO held by the validators become inputs to Bittensor's consensus mechanism called Yuma Consensus. YC drives validators towards a consensus, agreement about the value of the work done by miners. The miners with the highest agreed upon scores are minted TAO, the network digital currency.
Miners within this subnet are evaluated based on the number of times the model they have hosted has a lower loss than another model on the network within the context of a competition. To perform well, miners must attain the lowest loss on the largest number of random batches. For each competition, finding the best model and delta at the earliest block ensures the most incentive.
Note that competitions are specified independently here with a defined split of emissions from the subnet. Competitions each have unique parameters that define which model(s), tokenizer(s), size(s), sequence length(s) and more that miners will be evaluated against.
TL;DR:
This repo's main conversation is carried out in the Bittensor Discord. Visit the subnet 37 channel to ask questions and get real time feedback. You can view the ongoing running of the incentive mechanism, the best miners (see 'incentive'), the most in-consensus validators (see 'vtrust') using this taostats link. The table shows all 256 participant UIDs with corresponding YC stats and earnings.
See Miner Setup to learn how to setup a Miner.
See Validator Setup to learn how to setup a Validator.
We welcome feedback!
If you have a suggestion, please reach out on the Discord channel, or create an Issue in this repo.
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