LLamaSharp

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LLamaSharp is an Open Source & Models tool that provides a C#/.NET library for running Large Language Models (LLMs) like LLaMA and LLaVA locally. It enables efficient inference on local devices, supporting both CPU and GPU.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is LLamaSharp?

LLamaSharp is a powerful cross-platform C#/.NET library designed to efficiently run Large Language Models (LLMs) such as LLaMA and LLaVA directly on your local device. Built upon `llama.cpp`, it offers high-performance inference on both CPU and GPU, making it accessible for developers. The library provides higher-level APIs and RAG (Retrieval Augmented Generation) support, simplifying the deployment of LLMs within applications. It includes integrations for popular frameworks like semantic-kernel, kernel-memory, BotSharp, and Langchain, facilitating diverse application development. LLamaSharp supports various backends including CPU, CUDA, Metal, and Vulkan, and uses the GGUF model format, with guidance provided for model preparation and conversion.

Best used for

Ideal for developers who need to integrate Large Language Models into their C#/.NET applications, perform efficient local inference on various devices, and leverage RAG capabilities. Especially valuable for building AI-powered applications without relying on cloud-based LLM services.

Common actions

run LLMs locally

integrate AI models

develop .NET applications

perform local inference

face swappingcollaborationworkflows"AI Agents"low-code/no-codegithub copilotopen-sourceautomated workflowdeepfake

Capabilities

Key features

Local LLM inference
C#/.NET library
CPU/GPU support
RAG support
Multi-backend support
GGUF model format

Target Audience

developerdata scientist

Integrations

semantic-kernelkernel-memorybotsharplangchainmain.net

Pricing & Plans

Open Source

Free

FAQs

What types of LLMs can LLamaSharp run?

LLamaSharp is designed to run LLaMA and LLaVA models, along with others compatible with the `llama.cpp` project. It specifically uses the GGUF model format for efficient local inference on your device.

Does LLamaSharp support GPU acceleration?

Yes, LLamaSharp supports GPU acceleration through various backends including CUDA (versions 11 and 12), Metal for Mac, and Vulkan for Windows and Linux. Users need to install the appropriate backend package and set `GpuLayerCount`.

How do I get models for LLamaSharp?

You can find pre-converted GGUF format models on Huggingface by searching for the model name plus 'gguf'. Alternatively, you can convert PyTorch or Huggingface format models to GGUF yourself using Python scripts provided by `llama.cpp`.

What are the system requirements for LLamaSharp?

LLamaSharp is cross-platform, supporting Windows, Linux, and Mac. It requires a .NET environment and can run on CPU, but for optimal performance, a compatible GPU with sufficient memory is recommended, especially for larger models.

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