llyra 0.1.2

A lightweight interface for calling LLMs with strategy.

Llyra

Lightweight LLaMA Reasoning Assistant

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✨ Features

• Minimal, Configurable Inference
  Load prompts, model parameters, and tools from external files.

• Prompt Engineering Friendly
  Easily manage system prompts, roles, and chat formats through external .json or .txt files.

• Optional RAG Integration (Coming Soon)
  Native support for Weaviate-based retrieval-augmented generation.

• Hybrid Backend Support (Planned)
  Use local llama-cpp-python or connect to a remote Ollama endpoint via the same interface.

• Tool Support (Planned)
  Enable LLMs to use JSON-defined tools (function-calling style) with one argument.

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⚙️ Dependencies

Llyra does not bundle any backend inference engines. You must install them manually according to your needs:

Required (choose one):

• For local models: llama-cpp-python
• For remote inference: any Ollama-compatible API

Optional:

• For RAG: pip install weaviate-client

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📦 Installation

pip install https://github.com/albus-shore/Llyra/releases/download/v0.1.1/llyra-0.1.1-py3-none-any.whl

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🚀 Quickstart

1. Make directary config/ in your project root.
2. Add config.json and stategy.json to config/ directory.
3. Make directary models/ in your project root.
4. Rename your GGUF file as model.gguf and place it under models/ directory.
5. Make your first iterative chat inference with follwing example:

from llyra import Model

model = Model()

response = model.chat('Evening!',keep=True)

print(response)

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🛠 Configuration Example

config.json

{
    "model": "model",
    "directory": "models/",
    "strategy": "config/strategy.json",
    "gpu": false,
    "format": null,
    "ram": false
}

strategy.json:

[{
    "type": "chat",
    "role": {
        "input": "user",
        "output": "assistant"
        },
    "stop": "<|User|>",
    "max_token": 128e3,
    "temperature": 0.6
}]

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🧭 Roadmap

Phase	Feature	Status
1	Minimal llama-cpp-python local chat	✅ Finished
2	Predefined prompts via .txt / .json	✅ Finished
3	Weaviate RAG support	🔄 Ongoing
4	Ollama remote API support	⏳ Planned
5	Tool/function-calling via JSON	⏳ Planned

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🪪 License

This project is licensed under the MIT License.

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📚 Attribution

Currently, this package is built on top of the following open-source libraries:

• llama-cpp-python — licensed under the MIT License
  Python bindings for llama.cpp

This package does not include or redistribute any third-party source code.
All dependencies are installed via standard Python packaging tools (e.g. pip).

We gratefully acknowledge the authors and maintainers of these libraries for their excellent work.

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🌐 About the Name

Llyra is inspired by the constellation Lyra, often associated with harmony and simplicity.
In the same way, this package aims to bring harmony between developers and language models.

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Designed with care. Built for clarity.