Llama 3.1 Nemotron 92 B Instruct H F early LLM Model

Introduction

LLAMA-3.1-Nemotron-92B-Instruct-HF is a pre-trained language model generated by merging several existing models using the Mergekit tool. It is designed for text generation tasks and leverages the capabilities of multiple models to enhance performance.

Architecture

The model is developed using the transformers library. It is a composite of multiple instances of the Nvidia Llama-3.1-Nemotron-70B-Instruct-HF model, which have been combined using a specific merging technique known as the passthrough merge method. This process involves utilizing different layer ranges from the base model to create an enhanced model with improved capabilities.

Training

The training involves a strategic configuration using the YAML format, with the dtype set to bfloat16. The model is constructed by slicing various ranges of layers from the base model (nvidia/Llama-3.1-Nemotron-70B-Instruct-HF) and assembling these slices to form the final model. The specified layer ranges include:

Layers 0-10
Layers 5-15
Layers 10-20
Layers 15-25
Layers 20-30
Layers 25-80

Guide: Running Locally

To run LLAMA-3.1-Nemotron-92B-Instruct-HF locally, follow these steps:

Installation: Ensure you have Python and the transformers library installed. You can install the library using pip:
```
pip install transformers
```
Model Download: Download the model from its repository or use the Hugging Face Transformers API to load it directly.
Setup Environment: Configure your environment to support bfloat16 data type and ensure you have sufficient computational resources.
Inference: Implement a script to perform text generation using the model.

For optimal performance, it is recommended to use a cloud GPU service, such as AWS, Google Cloud, or Azure, which provides powerful GPUs suited for handling large models.

License

The licensing information for LLAMA-3.1-Nemotron-92B-Instruct-HF should be obtained from its repository or the license file accompanying the model. Ensure compliance with any terms specified for use, modification, and distribution.

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