Instructions to use KotshinZ/gpt2-RMT-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KotshinZ/gpt2-RMT-2 with Transformers:
# Load model directly from transformers import RecurrentMemoryTransformer model = RecurrentMemoryTransformer.from_pretrained("KotshinZ/gpt2-RMT-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| base_model: openai-community/gpt2 | |
| datasets: HuggingFaceFW/fineweb-edu | |
| library_name: transformers | |
| model_name: gpt2-RMT-2 | |
| tags: | |
| - generated_from_trainer | |
| - open-r1 | |
| - trl | |
| - sft | |
| licence: license | |
| license: apache-2.0 | |
| # Model Card for gpt2-RMT-2 | |
| This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on the [HuggingFaceFW/fineweb-edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) dataset. | |
| It has been trained using [TRL](https://github.com/huggingface/trl). | |
| For use this model. You need clone [KotShinZ/Recurrent-Memory-Transformer_PreTrained.git](https://github.com/KotShinZ/Recurrent-Memory-Transformer_PreTrained) | |
| ## Quick start | |
| ```python | |
| from transformers import pipeline | |
| question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" | |
| generator = pipeline("text-generation", model="KotshinZ/gpt2-RMT-2", device="cuda") | |
| output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] | |
| print(output["generated_text"]) | |
| ``` | |
| ## Training procedure | |
| [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/shin2021001-osaka-city-university/huggingface/runs/5xyxy6y1) | |
| This model was trained with SFT. | |
| This model memory_size = 10, n_backward = 2. | |
| ### Framework versions | |
| - TRL: 0.15.2 | |
| - Transformers: 4.50.0.dev0 | |
| - Pytorch: 2.5.1 | |
| - Datasets: 3.3.2 | |
| - Tokenizers: 0.21.0 | |
| ## Citations | |
| Cite TRL as: | |
| ```bibtex | |
| @misc{vonwerra2022trl, | |
| title = {{TRL: Transformer Reinforcement Learning}}, | |
| author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, | |
| year = 2020, | |
| journal = {GitHub repository}, | |
| publisher = {GitHub}, | |
| howpublished = {\url{https://github.com/huggingface/trl}} | |
| } | |
| ``` |