Update README.md

README.md

@@ -6,9 +6,35 @@ tags:
 - community-training
 metrics:
 - loss
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-# HRM Community Model
+HRM-LLM: A truly decentralized, human-like reasoning model built by the community
+
+HRM-LLM is a community-driven large language model powered by the Hierarchical Reasoning Model (HRM) architecture. It aims to be truly decentralized: anyone can train, contribute, and scale it forward from anywhere. HRM-LLM is designed to think and work like a human—iterating, refining, and allocating compute adaptively—so it learns efficiently and generalizes across tasks.
+
+Why HRM-LLM?
+- Human-like reasoning core: HRM brings hierarchical representations and adaptive computation to mimic iterative human thinking and planning.
+- Adaptive Computation Time (ACT): The model dynamically decides how much “thought” to spend per token—more for hard tokens, less for easy ones.
+- Decentralized and scalable: Anyone can hop in, train a few steps, and push a unified checkpoint to the Hub. Every contribution compounds.
+- Simple, hackable stack: PyTorch + Transformers + Datasets. Easy to extend, easy to improve.
+- Community-aligned progress: Transparent training, open checkpoints, and community governance.
+
+What this model aims to do
+- Break down complex problems into stages, reason across them, and refine answers over multiple internal steps.
+- Learn efficient patterns via ACT, saving compute where possible and spending it where it matters most.
+- Become a robust, general-purpose assistant shaped by its global community of contributors.
+
+How you can help
+- Train a few steps in Colab (or locally) and push your contribution.
+- Experiment with hyperparameters, tokenizers, datasets, or new HRM blocks.
+- Share insights and logs to improve the next iteration.
+
+License
+- This project is licensed under Apache-2.0. You’re free to use, modify, and distribute—with attribution and notice.
+
+Jump in and train
+- Colab (1-click): https://colab.research.google.com/drive/1xZNYC-yhwdJxzbpwRekE_rDjTki5CvEv?usp=sharing
+
+Quick start: contribute training from your environment
+Run this to join training and push your contribution to the shared checkpoint.
 
-Epoch: 1
-Validation Loss: 11.5441
+That’s it—share the Colab link, invite contributors, and let the community grow HRM-LLM together.