Instructions to use Drjkedwards/Recursive-Transformer-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Drjkedwards/Recursive-Transformer-Model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Drjkedwards/Recursive-Transformer-Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Verified signature```markdown # ERS — Enhanced Reconsideration System **A production-ready Python library turning stateless AI agents into stateful ones** by providing recursive reconsideration, knowledge-graph backed memory, and robust tensor/embedding workflows (PMLL-inspired). **Linked to the Kaggle PyTorch model:** [Recursive Transformer Model (RTM) / ERS PyTorch Implementation](https://www.kaggle.com/models/josefedwards/recursive-transformer-model/pyTorch) --- ## Table of Contents - [Overview](#overview) - [Key Features](#key-features) - [Architecture & Core Components](#architecture--core-components) - [Installation](#installation) - [Quick Start](#quick-start) - [Configuration & Persistence](#configuration--persistence) - [API Summary](#api-summary) - [Development & Testing](#development--testing) - [Roadmap](#roadmap) - [Contributing](#contributing) - [License](#license) - [Acknowledgements](#acknowledgements) --- ## Overview ERS (Enhanced Reconsideration System) is a memory-management…e183a48 -
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