A fast and intuitive tool for visualizing and analyzing model structures from safetensors files
Project description
TensorKiko
A fast and intuitive tool for visualizing and analyzing model structures from safetensors files, supporting tree-based visualizations and detailed parameter analysis.
Installation
Using pip
pip install tensorkiko
Using Homebrew
brew tap takara-ai/tensorkiko https://github.com/takara-ai/TensorKiko
brew install tensorkiko
Usage
After installation, you can use TensorKiko from the command line:
tensorkiko path/to/your/model.safetensors
For more options, use:
tensorkiko --help
Features
- Load and process safetensors files
- Generate interactive HTML visualizations of model structures
- Analyze model parameters, memory usage, and estimated FLOPs
- Search functionality for easy navigation of large models
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
License
This project is licensed under the MIT License.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file tensorkiko-0.1.6.tar.gz.
File metadata
- Download URL: tensorkiko-0.1.6.tar.gz
- Upload date:
- Size: 11.9 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.11.10
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
c57de03bc910607b8586c93f5581afd1afc3bf8df2161cfe19402ff0634de9f7
|
|
| MD5 |
33f5c70e51db1454ba4173f468a15783
|
|
| BLAKE2b-256 |
f1c567c7ca30f0deee2547e573da3c86a7264f98ec104795b9bd2e650630761e
|
File details
Details for the file tensorkiko-0.1.6-py3-none-any.whl.
File metadata
- Download URL: tensorkiko-0.1.6-py3-none-any.whl
- Upload date:
- Size: 11.4 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.11.10
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
4d16b774574c3bc6528e0e4f0de027219d6546a421ad977a3660a11d53bcba9a
|
|
| MD5 |
0618991df491c2d392cceee7510534bf
|
|
| BLAKE2b-256 |
56c1da585edf042cd93f4f2504084d8b6d11158fa44bd23e877b035bc6b14d97
|
