Llama 3.3 70 B Instruct ablated G G U F

Introduction

The Llama-3.3-70B-Instruct-ablated-GGUF is a quantized model designed for text generation tasks. It supports multiple languages and offers various quantization options to suit different hardware configurations. The model is based on the Llama architecture and has been tailored for chat and instructive tasks, ensuring flexibility and performance efficiency.

Architecture

This model utilizes the Llama-3 architecture, specifically designed to handle conversational and instructive text generation. It offers several quantization types, including Q8_0, Q6_K, and Q4_K_M, each optimized for different quality and performance needs.

Training

The model is trained using the imatrix option provided by the llama.cpp framework, with a specially curated dataset. The training process includes quantization techniques to optimize model performance across various hardware settings.

Guide: Running Locally

1. Install Prerequisites:
   Ensure you have Python installed along with the huggingface_hub CLI tool. Install it using:

   pip install -U "huggingface_hub[cli]"
   

2. Download the Model:
   Use the Hugging Face CLI to download the desired quantized model file. For example, to download the Q4_K_M version, run:

   huggingface-cli download bartowski/Llama-3.3-70B-Instruct-ablated-GGUF --include "Llama-3.3-70B-Instruct-ablated-Q4_K_M.gguf" --local-dir ./
   

3. Select Appropriate Quantization:
   Choose a quantization file that fits your system's RAM and VRAM capabilities. For maximum speed, ensure the model fits entirely within your GPU's VRAM.

4. Run the Model:
   Use the downloaded file with your preferred inference framework, such as LM Studio, to execute text generation tasks.

5. Cloud GPUs:
   For enhanced performance, consider using cloud services that provide high-capacity GPUs, such as AWS, Google Cloud, or Microsoft Azure.

License

The model is distributed under the llama3 license, which governs its usage and distribution. Users are encouraged to review the license terms to ensure compliance with all stipulations.