Falcon3 Mamba 7 B Instruct G G U F

Introduction

Falcon3-Mamba-7B-Instruct is a Large Language Model (LLM) designed for efficient inference on consumer-grade hardware. It utilizes the GGUF format to optimize performance, allowing users to run powerful LLMs locally on devices like laptops or even phones.

Architecture

The model is built on the Falcon3 architecture and is part of the Falcon3-Mamba family. It is tailored to provide high efficiency and accessibility, making it suitable for various applications in natural language processing and AI-powered text generation.

Training

Details specific to the training process of Falcon3-Mamba-7B-Instruct are not provided in the available documentation. However, the focus is on enabling the model to perform tasks with efficiency on consumer hardware.

Guide: Running Locally

To run Falcon3-Mamba-7B-Instruct locally, follow these steps:

1. Download GGUF Models:

   • Use the huggingface_hub library or manually download the model from Hugging Face:

     pip install huggingface_hub
     huggingface-cli download {model_name}
     

   • Replace {model_name} with the actual model name from your Hugging Face repository.

2. Install llama.cpp:

   • Build from Source:

     git clone https://github.com/ggerganov/llama.cpp
     cd llama.cpp
     cmake -B build
     cmake --build build --config Release
     

   • Download Pre-built Binaries: Check the llama.cpp repository for binaries suitable for your OS.
   • Use Docker: Refer to the llama.cpp documentation for Docker usage instructions.

3. Start Using the Model:

   • Text Completion:

     llama-cli -m {path-to-gguf-model} -p "I believe the meaning of life is" -n 128
     

   • Conversation Mode:

     llama-cli -m {path-to-gguf-model} -p "You are a helpful assistant" -cnv -co
     

For enhanced performance, it is recommended to use cloud GPUs from providers like AWS, Google Cloud, or Azure.

License

The licensing details for Falcon3-Mamba-7B-Instruct are not explicitly mentioned in the provided documentation. Users should refer to the Hugging Face repository or contact the Technology Innovation Institute for specific licensing information.