| --- |
| license: apache-2.0 |
| library_name: transformers |
| pipeline_tag: text-classification |
| base_model: jhu-clsp/mmBERT-small |
| base_model_relation: finetune |
| inference: false |
| tags: |
| - routing |
| - llm-routing |
| - prompt-classification |
| - modernbert |
| - multilingual |
| --- |
| |
| # s1d1-router, six sources, tiny, PyTorch fp16 (safetensors) |
|
|
| A "System 1" router: it reads a user prompt and returns its complexity score (0.01 to 1.00), its task type (19 classes) |
| and whether it needs tools. It never picks a model; the deployer maps these outputs to models with rules. The method, |
| the training data, the full results and the limitations are in the card of the main repository, |
| [scombat/s1d1-router](https://huggingface.co/scombat/s1d1-router). An open, replayable bench of the variants against three local LLM |
| judges on 360 multilingual prompts, with confidence intervals and witnesses, is |
| [s1d1-bench](https://github.com/scombat/s1d1-bench). |
|
|
| - Variant: `v7_small_l6-fp16`, mmBERT-small cut to 6 layers, 32k vocabulary, distilled from the full model. |
| - Format: PyTorch fp16 (safetensors), 85 MB. |
| - Outputs: 21 logits. Output 0 is the complexity in tier units (score = 0.5 + 0.25 x output, clipped to 0.01..1.00), |
| outputs 1 to 19 are the task logits (order in `task_names` of `config.json`), output 20 is the needs_tools logit. |
| - Pruned vocabulary variants ship `vocab_map.npy` (full tokenizer id -> pruned id); the loader below applies it. |
| - **Do not read it through `pipeline("text-classification")` with its defaults, nor through Text Embeddings |
| Inference**: both apply one softmax over the 21 outputs. `config.json` declares `problem_type: regression`, so |
| `pipeline(..., top_k=None)` returns the raw outputs; decode them as the main card shows. |
|
|
| ## Measures (validation, 4,914 rows, six sources, unweighted) |
|
|
| | complexity QWK | complexity MAE | task accuracy | task macro-F1 | needs_tools F1 | needs_tools AUROC | |
| |---|---|---|---|---|---| |
| | 0.649 | 0.091 | 0.782 | 0.662 | 0.484 | 0.904 | |
|
|
| Agreement with the full fp32 model on validation: same complexity tier 79.6%, same task |
| 81.3%, same needs_tools 96.7%. |
| Latency, batch 1, transformers backend on an Apple M4 Max: p50 3.1 ms, p95 8.0 ms. |
| Full results and method: `docs/15-catalog-v4-v7.md` in the project; `eval.json` in this repo. |
| |
| ## Use |
| |
| `s1d1_router.py` from [scombat/s1d1-router](https://huggingface.co/scombat/s1d1-router) loads this repository directly (Python >= 3.10, |
| `tokenizers`, `huggingface_hub` and `torch`): |
|
|
| ```python |
| from s1d1_router import Router |
| router = Router("scombat/s1d1-router-6src-tiny") |
| router.route(["Quelle est la capitale de l'Australie ?"]) |
| ``` |
|
|
| ## License |
|
|
| Apache-2.0. The base model, [mmBERT-small](https://huggingface.co/jhu-clsp/mmBERT-small), is MIT-licensed; its |
| notice is kept in `NOTICE`. The license requires you to keep `LICENSE` and `NOTICE` when you redistribute the weights or |
| a derivative; it does not require attribution when you merely use the model in a product or behind an API. If you use |
| it in published work or a product, a mention of the author and the citation below are appreciated (a request, not a |
| condition). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{s1d1_router_2026, |
| title = {s1d1-router: a one-pass multilingual router for LLM requests}, |
| author = {scombat}, |
| year = {2026}, |
| howpublished = {\url{https://huggingface.co/scombat/s1d1-router}} |
| } |
| ``` |
|
|
