metadata
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.
| 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))