Multilingual Saiga Suzume 8 B G G U F

Introduction

The Multilingual-SaigaSuzume-8B-GGUF is a quantized model derived from the Khetterman/Multilingual-SaigaSuzume-8B, featuring multilingual capabilities across 11 languages. It is designed to integrate well with other models, focusing on conversational applications.

Architecture

This model is a result of merging seven different models using the mergekit tool. The architecture leverages bfloat16 data type and includes models like huihui-ai/Meta-Llama-3.1-8B-Instruct-abliterated, IlyaGusev/saiga_llama3_8b, and several variations of lightblue/suzume-llama-3-8B-multilingual. The model supports languages including German, English, Spanish, French, Hindi, Italian, Japanese, Portuguese, Russian, Thai, and Chinese.

Training

The model was built using a combination of pre-existing models, utilizing a simple merge method called model_stock. This approach involved the integration of different configurations and structures to produce a single, unified model with enhanced multilingual capabilities.

Guide: Running Locally

1. Installation: Ensure you have the Hugging Face Transformers library installed. You can install it using pip:

   pip install transformers
   

2. Download the Model: Use the Hugging Face Hub to download the model files.
3. Load the Model: Load the model using the Transformers library in your Python environment:

   from transformers import AutoModel
   model = AutoModel.from_pretrained("QuantFactory/Multilingual-SaigaSuzume-8B-GGUF")
   

4. Execution: Run inference tasks using the loaded model in your local environment.

Cloud GPUs

For optimized performance, it is recommended to use cloud GPU services such as AWS, Google Cloud, or Azure, which provide scalable and high-performance computing resources.

License

The model and its components are provided under the terms specified by the respective authors and contributors of the original models. Users are advised to review these licenses to ensure compliance with their requirements.