Use cases

This model is used to deep clean the Rhino dataset, making it a higher quality dataset. This model achieved an average MSE loss of 0.095 during training. We recommend to use the sigmoid function to turn the logits into probabilities:

1 / (1 + torch.exp(logits))

Training

Using trl's RewardTrainer, this model was trained on berkeley-nest/Nectar. The dataset is curated on-the-fly during training, as explained in the Rhino repo.

Downloads last month
11
Inference Providers NEW
This model is not currently available via any of the supported Inference Providers.

Model tree for M4-ai/TinyMistral-248M-v2-cleaner

Merges
1 model

Dataset used to train M4-ai/TinyMistral-248M-v2-cleaner