Deep Seek V3 slice jp64 gguf

Introduction

The DeepSeek-V3-Slice-JP64-GGUF is a model based on DeepSeek-V3, specifically optimized for Japanese language tasks. It uses a Mixture of Experts (MoE) approach, selecting experts at each layer based on common examples in Japanese. This model is provided in the GGUF format, converted from the original format to accommodate specific use cases.

Architecture

The model architecture is derived from the DeepSeek-V3 model, employing the MoE technique to optimize for frequently occurring examples in Japanese. This involves a careful selection and reconfiguration of experts at each layer, making the model highly tuned for its intended language tasks.

Training

This model was trained using the TFMC/imatrix-dataset-for-japanese-llm dataset, which is specifically curated for Japanese language model training. This dataset provides a robust foundation for the model's language capabilities, ensuring high relevance and accuracy in its outputs.

Guide: Running Locally

To run the model locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/ggerganov/llama.cpp.git
   

2. Navigate to the directory:

   cd llama.cpp
   

3. Build the project with CUDA support:

   cmake -B build -DGGML_CUDA=ON
   cmake --build build --config Release
   

4. Execute the model using the command-line interface (CLI):

   build/bin/llama-cli -m 'c4ai-command-r7b-12-2024-Q4_0-00001-of-00010.gguf' -n 128 -c 128 -p 'あなたはプロの料理人です。レシピを教えて' -cnv
   

For enhanced performance, consider utilizing cloud GPUs from providers like AWS, Google Cloud, or Azure, which can offer significant computational power for model inference.

License

The model adheres to the licensing terms of the original DeepSeek-V3 model, provided by deepseek-ai. Ensure compliance with these terms when using or distributing the model.