ModernBERT-large-llm-router
This model is a fine-tuned version of answerdotai/ModernBERT-large on DevQuasar/llm_router_dataset-synth.
It achieves the following results on the test set:
- Loss: 0.0536
- F1: 0.9933
Model description
See original answerdotai/ModernBERT-large model card for additional information. This model is intended to classify queries for LLM routing. More advanced queries get labeled 1 for large_llm and simpler queries get 0 for small_llm.
Training procedure
Annotated training procedure available in this notebook. Methodology and code credits to Phillip Schmid from his Fine-tune classifier with ModernBERT in 2025 blog post.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.0303 | 1.0 | 479 | 0.0317 | 0.9881 |
| 0.014 | 2.0 | 958 | 0.0374 | 0.9927 |
| 0.0044 | 3.0 | 1437 | 0.0502 | 0.9921 |
| 0.0004 | 4.0 | 1916 | 0.0554 | 0.9927 |
| 0.0003 | 5.0 | 2395 | 0.0536 | 0.9933 |
Framework versions
- Transformers 4.48.0.dev0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.21.0