---
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))
```

## Citation

<!-- TODO: add BibTeX. -->