Beyond Magnitude: Contrastive Routing for Modular Mixture-of-Experts
Paper • 2609.01100 • Published • 1
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ilsp/CoRM", device_map="auto")Quick Links
CoRM
Checkpoints for the paper Beyond Magnitude: Contrastive Routing for Modular Mixture-of-Experts.
| Model | Active Params | Total Params | Routing | Link |
|---|---|---|---|---|
| CoRM-182M | 182M | 777M | Top-1 | link |
| CoRM-182M | 266M | 777M | Top-2 | link |
| CoRM-469M | 469M | 2.58B | Top-1 | link |
Usage
These models use custom modeling code, so trust_remote_code=True is required.
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))
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ilsp/CoRM")