Integrate llama.cpp-based local LLM inference into Go applications using yzma.
npx skills add https://github.com/czyt/claude-skills --skill yzma
Please help me install this Agent Skill. Skill: yzma Source: https://github.com/czyt/claude-skills/tree/main/skills/yzma Command: npx skills add https://github.com/czyt/claude-skills --skill yzma
This Skill simplifies the integration of local Large Language Models (LLMs) into Go applications by providing clear guidance on using the yzma library, which acts as a wrapper for llama.cpp.
Use the yzma skill to install the yzma CLI tool and download the llama.cpp libraries for CUDA acceleration.
High-intent search queries and answers about installing and using this skill.
To run local LLM inference in Go, use the yzma library to integrate llama.cpp, enabling text generation and chat directly within your application without external servers. You must install the yzma CLI and llama.cpp libraries.
Yes, yzma supports hardware acceleration for local model inference by leveraging CUDA, Metal, and Vulkan. This allows you to significantly speed up text generation and embedding tasks on supported hardware configurations.
Yes, you can use vision models and tool calling with local LLMs in Go. The yzma library integrates with llama.cpp to support these features alongside text generation, chat, and embeddings using GGUF models.
Before loading GGUF models in Go, you need to install the yzma CLI tool and download the llama.cpp libraries. This setup allows you to configure models, handle context, and tune parameters for local inference.
The best way to integrate llama.cpp with Go for text generation is using the yzma library wrapper. It simplifies model management and hardware acceleration configuration, allowing direct execution of local models on your machine.
You generate text embeddings locally using Go by utilizing the yzma library, which wraps llama.cpp. This allows you to load GGUF models and compute embeddings directly on your machine with hardware acceleration.
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