MoWE Paper
Collection
Releases associated with the MoWE paper written by Tavish, Vardhaman, Keshava, Dr. Deepan. Coded and trained with openlanguagemodel. • 3 items • Updated
Inference checkpoint for the dense baseline released with Mixture of Wide Experts.
| Property | Value |
|---|---|
| Parameters | 103.46M |
| Transformer layers | 19 |
| Model width | 768 |
| Attention heads | 12 |
| FFN hidden width | 455 |
| Context length | 1,024 |
| Tokenizer | GPT-2 |
The checkpoint is an inference-only safetensors state dictionary. It contains
no optimizer state. This is a base language model and is not instruction-tuned.
Install the companion MoWE extension for OpenLanguageModel, then load this repository directory:
import torch
from mowe import load_checkpoint
model = load_checkpoint(
"/path/to/dense-768-19l",
device="cuda",
dtype=torch.bfloat16,
).eval()
This is a small research model. It may produce incorrect, biased, unsafe, or memorized text and should not be used as a production assistant without separate evaluation and safeguards.
Released under the Apache License 2.0. Please cite the accompanying MoWE paper.