Downloads · 30 days
18
1% of all-time downloads
Ex0bit/hrm-demo
hrm-demo is a text generation model from Ex0bit. Use it when you need the model to write or continue text. It is set up for pytorch. The card lists the license as mit.
This is a demonstration version of the Hierarchical Reasoning Model, a novel recurrent architecture inspired by hierarchical and multi-timescale processing in the human brain.
Downloads · 30 days
18
1% of all-time downloads
All-time downloads
1.9K
Public
Repo size
117 MB
Likes
4
Public
Click a slice to open those files.
.pth117 MB · 100%
From the Hugging Face model README
This is a demonstration version of the Hierarchical Reasoning Model, a novel recurrent architecture inspired by hierarchical and multi-timescale processing in the human brain.
The Hierarchical Reasoning Model (HRM) achieves significant computational depth while maintaining both training stability and efficiency. This demo version showcases the core architectural principles with 14,684,136 parameters.
import torch
from demo_hrm import DemoHRM
# Load model
model = DemoHRM(hidden_size=512, num_layers=6, vocab_size=1000)
model.load_state_dict(torch.load('model.pth'))
model.eval()
# Generate reasoning output
input_ids = torch.randint(0, 1000, (1, 20)) # batch_size=1, seq_len=20
with torch.no_grad():
output = model(input_ids)
predictions = torch.softmax(output, dim=-1)
This demo model was trained on simulated reasoning tasks including:
@misc{wang2025hierarchicalreasoningmodel,
title={Hierarchical Reasoning Model},
author={Guan Wang and Jin Li and Yuhao Sun and Xing Chen and Changling Liu and Yue Wu and Meng Lu and Sen Song and Yasin Abbasi Yadkori},
year={2025},
eprint={2506.21734},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2506.21734},
}
This demo model demonstrates the core concepts of the Hierarchical Reasoning Model. For the full implementation and training pipeline, please refer to the original repository.
Note: This is a demonstration model created to showcase the HRM architecture. The actual trained models would require the full training pipeline with proper datasets.