Model card updated after epoch 0

README.md

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+---
+base_model: t5-small
+tags: [hrm, act, dolly-15k]
+metrics: [loss, perplexity]
+---
+# HRM-Text1
+
+**HRM-Text1** is an experimental instruction-following text generation model based on the **Hierarchical Recurrent Memory (HRM)** architecture. It is trained on the `databricks/databricks-dolly-15k` dataset, which consists of instruction–response pairs across multiple task types.
+
+The model utilizes the HRM structure, consisting of a "Specialist" module for low-level processing and a "Manager" module for high-level abstraction and planning. This architecture aims to handle long-range dependencies more effectively by summarizing information at different temporal scales.
+
+## Model Description
+
+- **Architecture:** Hierarchical Recurrent Memory (HRM)
+- **Training Data:** [databricks/databricks-dolly-15k](https://hf.co/datasets/databricks/databricks-dolly-15k)
+- **Original Paper:** [Hierarchical Reasoning Model](https://arxiv.org/abs/2506.21734)
+- **Tokenizer:** `t5-small` (slow T5 SentencePiece)
+- **Vocab Size**: 32100
+- **Objective:** Causal Language Modeling
+
+### Latest Performance (Epoch 0)
+- **Validation Loss**: `4.5187`
+- **Validation Perplexity**: `91.72`