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license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
tags:
- mixture-of-experts
- moe
- corm
---
# CoRM
Checkpoints for the paper [Beyond Magnitude: Contrastive Routing for Modular Mixture-of-Experts](https://arxiv.org/abs/2609.01100).
| Model | Active Params | Total Params | Routing | Link |
|---|---|---|---|---|
| CoRM-182M | 182M | 777M | Top-1 | [link](https://huggingface.co/ilsp/CoRM-182M-top1) |
| CoRM-182M | 266M | 777M | Top-2 | [link](https://huggingface.co/ilsp/CoRM-182M-top2) |
| CoRM-469M | 469M | 2.58B | Top-1 | [link](https://huggingface.co/ilsp/CoRM-469M-top1) |
## Usage
These models use custom modeling code, so `trust_remote_code=True` is required.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "ilsp/CoRM-182M-top1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
inputs = tokenizer("The capital of Greece is", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=32)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Limitations
These are base models pretrained on The Pile; they are not instruction-tuned. The Hugging Face implementation does not use a KV cache and ignores `attention_mask`, so pass unpadded sequences.
## Citation
```bibtex
@misc{xiros2026magnitudecontrastiveroutingmodular,
title={Beyond Magnitude: Contrastive Routing for Modular Mixture-of-Experts},
author={Nikolaos Xiros and Dimitrios Damianos and Maria-Eleni Zoumpoulidi and Leon Voukoutis and Vassilis Katsouros and Georgios Paraskevopoulos},
year={2026},
eprint={2609.01100},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2609.01100},
}
```
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