metadata
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
- Original Paper: Hierarchical Reasoning Model
- Tokenizer:
t5-small(slow T5 SentencePiece) - Vocab Size: 32100
- Objective: Causal Language Modeling
Latest Performance (Epoch 19)
- Validation Loss:
3.6484 - Validation Perplexity:
38.41