Captain Eris_ Twighlight V0.420 12 B G G U F

Introduction

Captain-Eris_Twighlight-V0.420-12B-GGUF is a text generation model optimized through various quantization techniques to enhance performance on different hardware configurations. This model leverages the llama.cpp implementation for quantization, providing a range of quantized versions to suit various computational capabilities and quality requirements.

Architecture

The model is based on the Nitral-AI's Captain-Eris_Twighlight-V0.420-12B architecture. It supports multiple quantization formats, such as Q8_0, Q6_K, Q5_K, and others, aimed at optimizing for size and performance on CPUs and GPUs. The quantization process utilizes the llama.cpp framework, with specific adaptations for embedding and output weights to achieve high performance and efficiency.

Training

Quantizations were performed using the imatrix option, taking advantage of a specialized dataset for calibration. This approach ensures that the model maintains high-quality outputs even when reduced in size. The quantization methods are designed to accommodate various hardware environments, from high-end GPUs to low-memory CPUs.

Guide: Running Locally

1. Install huggingface-cli: Ensure you have the huggingface-cli installed by running:

   pip install -U "huggingface_hub[cli]"
   

2. Download Model: Use the following command to download the desired quantized model file:

   huggingface-cli download bartowski/Captain-Eris_Twighlight-V0.420-12B-GGUF --include "Captain-Eris_Twighlight-V0.420-12B-Q4_K_M.gguf" --local-dir ./
   

3. Hardware Considerations: For optimal performance, ensure the model fits into your GPU's VRAM. The file size should be 1-2GB smaller than your total VRAM. If using a combination of system RAM and VRAM, select a quant size accordingly.

4. GPU Recommendations: Cloud services like AWS, Google Cloud, or Azure offer GPU instances that can efficiently run such models.

License

The model follows the licensing terms of the Hugging Face repository. Users should refer to the specific model card on Hugging Face for detailed licensing information.