A C#/.NET library to run LLM (LLaMA/LLaVA) on your local device efficiently.
**LLamaSharp is a cross-platform library to run LLaMA model (and others) on your local device. Based on [llama.cpp](https://github.com/ggerganov/llama.cpp), inference with LLamaSharp is efficient on both CPU and GPU. With the higher-level APIs and RAG support, it's convenient to deploy LLMs (Large Language Models) in your application with LLamaSharp.**
**Please star the repo to show your support for this project!**
---
Table of Contents
## Documentation
- [Quick start](https://scisharp.github.io/LLamaSharp/latest/QuickStart/)
- [Ask AI via deep-wiki](https://deepwiki.com/SciSharp/LLamaSharp)
- [FAQ](https://scisharp.github.io/LLamaSharp/latest/FAQ/)
- [Tutorial](https://scisharp.github.io/LLamaSharp/latest/Tutorials/NativeLibraryConfig/)
- [Full documentation](https://scisharp.github.io/LLamaSharp/latest/)
- [API reference](https://scisharp.github.io/LLamaSharp/latest/xmldocs/)
## Console Demo
## Integrations & Examples
There are integrations for the following libraries, making it easier to develop your APP. These integrations are developed in their own repositories.
- [BotSharp](https://github.com/SciSharp/BotSharp): an open source machine learning framework for AI Bot platform builder.
- [Langchain](https://github.com/tryAGI/LangChain): a framework for developing applications powered by language models.
- [MaIN.NET](https://github.com/wisedev-code/MaIN.NET): simplistic approach to orchestrating agents/chats from different (llm) providers
The following examples show how to build APPs with LLamaSharp.
- [Official Console Examples](./LLama.Examples/)
- [Unity Demo](https://github.com/eublefar/LLAMASharpUnityDemo)
- [LLamaStack (with WPF and Web demo)](https://github.com/saddam213/LLamaStack)
- [Blazor Demo (with Model Explorer)](https://github.com/alexhiggins732/BLlamaSharp.ChatGpt.Blazor)
- [ASP.NET Demo](./LLama.Web/)
- [LLamaWorker (ASP.NET Web API like OAI and Function Calling Support)](https://github.com/sangyuxiaowu/LLamaWorker)
- [VirtualPet (Desktop Application)](https://github.com/AcoranGonzalezMoray/VirtualPet-WindowsEdition)
- [KaiROS AI (Local AI Assistant for Windows & Android)](https://github.com/avikeid2007/KaiROS-AI)
## Get started
### Installation
To gain high performance, LLamaSharp interacts with native libraries compiled from c++, these are called `backends`. We provide backend packages for Windows, Linux and Mac with CPU, CUDA, Metal and Vulkan. You **don't** need to compile any c++, just install the backend packages.
If no published backend matches your device, please open an issue to let us know. If compiling c++ code is not difficult for you, you could also follow [this guide](./docs/ContributingGuide.md) to compile a backend and run LLamaSharp with it.
1. Install [LLamaSharp](https://www.nuget.org/packages/LLamaSharp) package on NuGet:
```
PM> Install-Package LLamaSharp
```
2. Install one or more of these backends, or use a self-compiled backend.
- [`LLamaSharp.Backend.Cpu`](https://www.nuget.org/packages/LLamaSharp.Backend.Cpu): Pure CPU for Windows, Linux & Mac. Metal (GPU) support for Mac.
- [`LLamaSharp.Backend.Cuda11`](https://www.nuget.org/packages/LLamaSharp.Backend.Cuda11): CUDA 11 for Windows & Linux.
- [`LLamaSharp.Backend.Cuda12`](https://www.nuget.org/packages/LLamaSharp.Backend.Cuda12): CUDA 12 for Windows & Linux.
- [`LLamaSharp.Backend.Vulkan`](https://www.nuget.org/packages/LLamaSharp.Backend.Vulkan): Vulkan for Windows & Linux.
### Model preparation
There are two popular formats of model file of LLMs, these are PyTorch format (.pth) and Huggingface format (.bin). LLamaSharp uses a `GGUF` format file, which can be converted from these two formats. To get a `GGUF` file, there are two options:
1. Search model name + 'gguf' in [Huggingface](https://huggingface.co), you will find lots of model files that have already been converted to GGUF format. Please take note of the publishing time of them because some old ones may only work with older versions of LLamaSharp.
2. Convert PyTorch or Huggingface format to GGUF format yourself. Please follow the instructions from [this part of llama.cpp readme](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#prepare-and-quantize) to convert them with python scripts.
Generally, we recommend downloading models with quantization rather than fp16, because it significantly reduces the required memory size while only slightly impacting the generation quality.
### Example of LLaMA chat session
Here is a simple example to chat with a bot based on a LLM in LLamaSharp. Please replace the model path with yours.
```
…
```
For more examples, please refer to [LLamaSharp.Examples](./LLama.Examples).
## FAQ
#### Why is my GPU not used when I have installed CUDA?
1. If you are using backend packages, please make sure you have installed the CUDA backend package which matches the CUDA version installed on your system.
2. Add the following line to the very beginning of your code. The log will show which native library file is loaded. If the CPU library is loaded, please try to compile the native library yourself and open an issue for that. If the CUDA library is loaded, please check if `GpuLayerCount > 0` when loading the model weight.
```cs
NativeLibraryConfig.All.WithLogCallback(delegate (LLamaLogLevel level, string message) { Console.Write($"{level}: {message}"); } )
```
#### Why is the inference so slow?
Firstly, due to the large size of LLM models, it requires more time to generate output than other models, especially when you are using models larger than 30B parameters.
To see if that's a LLamaSharp performance issue, please follow the two tips below.
1. If you are using CUDA, Metal or Vulkan, please set `GpuLayerCount` as large as possible.
2. If it's still slower than you expect it to be, please try to run the same model with same setting in [llama.cpp examples](https://github.com/ggerganov/llama.cpp/tree/master/examples). If llama.cpp outperforms LLamaSharp significantly, it's likely a LLamaSharp BUG and please report that to us.
#### Why does the program crash before any output is generated?
Generally, there are two possible cases for this problem:
1. The native library (backend) you are using is not compatible with the LLamaSharp version. If you compiled the native library yourself, please make sure you have checked-out llama.cpp to the corresponding commit of LLamaSharp, which can be found at the bottom of README.
2. The model file you are using is not compatible with the backend. If you are using a GGUF file downloaded from huggingface, please check its publishing time.
#### Why is my model generating output infinitely?
Please set anti-prompt or max-length when executing the inference.
## Contributing
All contributions are welcome! There's a TODO list in [LLamaSharp Dev Project](https://github.com/orgs/SciSharp/projects/5) and you can pick an interesting one to start. Please read the [contributing guide](./CONTRIBUTING.md) for more information.
You can also do one of the following to help us make LLamaSharp better:
- Submit a feature request.
- Star and share LLamaSharp to let others know about it.
- Write a blog or demo about LLamaSharp.
- Help to develop Web API and UI integration.
- Just open an issue about the problem you've found!
## Join the community
Join our chat on [Discord](https://discord.gg/7wNVU65ZDY) (please contact Rinne to join the dev channel if you want to be a contributor).
Join [QQ group](http://qm.qq.com/cgi-bin/qm/qr?_wv=1027&k=sN9VVMwbWjs5L0ATpizKKxOcZdEPMrp8&authKey=RLDw41bLTrEyEgZZi%2FzT4pYk%2BwmEFgFcrhs8ZbkiVY7a4JFckzJefaYNW6Lk4yPX&noverify=0&group_code=985366726)
## Star history
## Contributor wall of fame
## Map of LLamaSharp and llama.cpp versions
If you want to compile llama.cpp yourself you **must** use the exact commit ID listed for each version.
| LLamaSharp | Verified Model Resources | llama.cpp commit id |
| - | -- | - |
| v0.2.0 | This version is not recommended to use. | - |
| v0.2.1 | [WizardLM](https://huggingface.co/TheBloke/wizardLM-7B-GGML/tree/previous_llama), [Vicuna (filenames with "old")](https://huggingface.co/eachadea/ggml-vicuna-13b-1.1/tree/main) | - |
| v0.2.2, v0.2.3 | [WizardLM](https://huggingface.co/TheBloke/wizardLM-7B-GGML/tree/previous_llama_ggmlv2), [Vicuna (filenames without "old")](https://huggingface.co/eachadea/ggml-vicuna-13b-1.1/tree/main) | `63d2046` |
| v0.3.0, v0.4.0 | [LLamaSharpSamples v0.3.0](https://huggingface.co/AsakusaRinne/LLamaSharpSamples/tree/v0.3.0), [WizardLM](https://huggingface.co/TheBloke/wizardLM-7B-GGML/tree/main) | `7e4ea5b` |
| v0.4.1-preview | [Open llama 3b](https://huggingface.co/SlyEcho/open_llama_3b_ggml), [Open Buddy](https://huggingface.co/OpenBuddy/openbuddy-llama-ggml)| `aacdbd4` |
|v0.4.2-preview | [Llama2 7B (GGML)](https://huggingface.co/TheBloke/llama-2-7B-Guanaco-QLoRA-GGML)| `3323112` |
| v0.5.1 | [Llama2 7B (GGUF)](https://huggingface.co/TheBloke/llama-2-7B-Guanaco-QLoRA-GGUF)| `6b73ef1` |
| v0.6.0 | | [`cb33f43`](https://github.com/ggerganov/llama.cpp/commit/cb33f43a2a9f5a5a5f8d290dd97c625d9ba97a2f) |
| v0.7.0, v0.8.0 | [Thespis-13B](https://huggingface.co/TheBloke/Thespis-13B-v0.5-GGUF/tree/main?not-for-all-audiences=true), [LLaMA2-7B](https://huggingface.co/TheBloke/llama-2-7B-Guanaco-QLoRA-GGUF) | [`207b519`](https://github.com/ggerganov/llama.cpp/commit/207b51900e15cc7f89763a3bb1c565fe11cbb45d) |
| v0.8.1 | | [`e937066`](https://github.com/ggerganov/llama.cpp/commit/e937066420b79a757bf80e9836eb12b88420a218) |
| v0.9.0, v0.9.1 | [Mixtral-8x7B](https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF) | [`9fb13f9`](https://github.com/ggerganov/llama.cpp/blob/9fb13f95840c722ad419f390dc8a9c86080a3700) |
| v0.10.0 | [Phi2](https://huggingface.co/TheBloke/phi-2-GGUF) | [`d71ac90`](https://github.com/ggerganov/llama.cpp/tree/d71ac90985854b0905e1abba778e407e17f9f887) |
| v0.11.1, v0.11.2 | [LLaVA-v1.5](https://hf-mirror.com/jartine/llava-v1.5-7B-GGUF/blob/main/llava-v1.5-7b-mmproj-Q4_0.gguf), [Phi2](https://huggingface.co/TheBloke/phi-2-GGUF)| [`3ab8b3a`](https://github.com/ggerganov/llama.cpp/tree/3ab8b3a92ede46df88bc5a2dfca3777de4a2b2b6) |
| v0.12.0 | LLama3 | [`a743d76`](https://github.com/ggerganov/llama.cpp/tree/a743d76a01f23038b2c85af1e9048ee836767b44) |
| v0.13.0 | | [`1debe72`](https://github.com/ggerganov/llama.cpp/tree/1debe72737ea131cb52975da3d53ed3a835df3a6) |
| v0.14.0 | Gemma2 | [`36864569`](https://github.com/ggerganov/llama.cpp/tree/368645698ab648e390dcd7c00a2bf60efa654f57) |
| v0.15.0 | LLama3.1 | [`345c8c0c`](https://github.com/ggerganov/llama.cpp/tree/345c8c0c87a97c1595f9c8b14833d531c8c7d8df) |
| v0.16.0 | | [`11b84eb4`](https://github.com/ggerganov/llama.cpp/tree/11b84eb4578864827afcf956db5b571003f18180) |
| v0.17.0 | | [`c35e586e`](https://github.com/ggerganov/llama.cpp/tree/c35e586ea57221844442c65a1172498c54971cb0) |
| v0.18.0 | | [`c35e586e`](https://github.com/ggerganov