https://github.com/scisharp/ggmlsharp
Use GGML with C#/.NET
https://github.com/scisharp/ggmlsharp
Last synced: about 1 year ago
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Use GGML with C#/.NET
- Host: GitHub
- URL: https://github.com/scisharp/ggmlsharp
- Owner: SciSharp
- License: mit
- Created: 2024-05-17T00:03:12.000Z (about 2 years ago)
- Default Branch: master
- Last Pushed: 2024-07-24T01:11:55.000Z (almost 2 years ago)
- Last Synced: 2024-07-24T03:39:13.747Z (almost 2 years ago)
- Language: C#
- Size: 29.1 MB
- Stars: 13
- Watchers: 4
- Forks: 4
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# GGMLSharp Introduction
GGMLSharp is a API for C# to use [ggml](https://github.com/ggerganov/ggml).
ggml is a wonderful C-language machine-learning tool, and now you can use it with C#.
GGMLSharp contains all ggml shared libs and some demos.
## Feature
- Written in C# only
- Only depends on ggml
- DotNet 462 Support!
- All Demos can use safe code only!
## Demos
### mnist_cpu
[mnist_cpu](./Demos/MNIST_CPU/) is a basic demo for learning how to use GGMLSharp. It contains two Linears.
### mnist_cnn
[mnist_cnn](./Demos/MNIST_CNN/) is a demos show how to use convolution. In this demo, there are two conv2d and pool max.
### mnist_train
[mnist_train](./Demos/MNIST_Train/) is a demo shows how to train a model. The model is same as mnist_cpu.
### simple_backend
[simple_backend](./Demos/SimpleBackend/) shows how to use GGMLSharp with cuda. In this demo, you shold take ggml.dll for cuda. You can get it with the help of [ggml](https://github.com/ggerganov/ggml) or you can download it from [llama.cpp](https://github.com/ggerganov/llama.cpp/releases).
### magika
[magika](./Demos/Magika/) is a useful tool from google. It can help to get the style of a file in high speed.
### Converter
[Converter](./Demos/Converter/) is a useful tool for converting llm models from bin/ckpt/safetensors to gguf without any python environment.
### ModelLoader
[ModelLoader](./Demos/ModelLoader/) is a tool for loading safetensors or pickle file directly from binary data. This demo can help to learn how to read a model file without any help of python.
### SAM
[SAM](./Demos/SAM/) (Segment Anything Model) can help us seprate things from an image.
### TestOpt
[TestOpt](./Demos/TestOpt/) is a basic demo for optimizar.
### Yolov3Tiny
[Yolov3Tiny](./Demos/Yolov3Tiny/) is a Demo shows how to implement YOLO object detection with ggml using pretrained model. The weight have been converted to gguf.