nanoDT
The simplest, fastest repository for training/finetuning medium-sized DTs.
This repository is based on nanoGPT and provides a lightweight implementation of Decision Transformers.
Features
- Lightweight and Simple: Minimal dependencies and easy-to-understand codebase.
- Continuous and Discrete Action Spaces: Works seamlessly with both types of environments.
- Customizable Models: Easily configure model architecture and training parameters.
- Dataset Integration: Supports Minari datasets for training.
- Scalable Training: Optimized for fast training on GPUs.
1 maintainer
Metadata
1 maintainer
Release files for nanodt 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| nanodt-0.1.0.tar.gz | 35.5 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| nanodt-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 50.8 kB
Release files / nanodt-0.1.0.tar.gz
| Download URL | nanodt-0.1.0.tar.gz |
|---|---|
| Size | 35.5 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
uv/0.7.5
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Release files / nanodt-0.1.0-py3-none-any.whl
| Download URL | nanodt-0.1.0-py3-none-any.whl |
|---|---|
| Size | 15.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
uv/0.7.5
|
