Captain Eris Diogenes_ Twilight V0.420 12 B G G U F

Introduction

Captain-Eris-Diogenes_Twilight-V0.420-12B-GGUF is a quantized model for text generation using the LLAMACPP and IMATRIX quantization techniques. This model is based on the original Nitral-AI/Captain-Eris-Diogenes_Twilight-V0.420-12B model and is optimized for efficient performance across different hardware configurations.

Architecture

The model uses the llama.cpp framework for quantization, specifically using the b4404 release. The quantization process employs the IMATRIX option, leveraging an extensive calibration dataset to produce various quantized versions of the model. These versions include different configurations of quantization schemas, such as Q8_0, Q6_K_L, and IQ4_XS, among others, each optimized for specific hardware and performance needs.

Training

Quantization was performed using the IMATRIX technique, which involves mapping the model's weights to lower precision formats without significantly compromising the model's performance. This results in different versions of the model, each with varying levels of quality and size, suitable for different computational capabilities.

Guide: Running Locally

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

   pip install -U "huggingface_hub[cli]"
   

2. Downloading the Model: Use the following command to download a specific quantized version of the model:

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

3. Hardware Considerations: For optimal performance, load the model onto a GPU. If using NVIDIA hardware, leverage cuBLAS; for AMD, use rocBLAS.

4. Running the Model: Load the model into an inference engine like LM Studio, which supports various quantization formats and provides necessary optimizations.

5. Cloud GPUs: If local hardware is insufficient, consider using cloud GPU services such as AWS EC2, Google Cloud GPU instances, or Azure's NV-series.

License

The model and its quantized versions are distributed under the terms outlined on the Hugging Face platform. Users should review the licensing details provided with the model to ensure compliance with usage restrictions and obligations.