M T3 Gen3 G M A gemma 2 9 B

Introduction

The MT3-Gen3-GMA-GEMMA-2-9B is a text generation model that combines multiple pre-trained language models using the SLERP merge method. It is designed for use with the Transformers library and supports inference endpoints.

Architecture

This model is a result of merging two distinct models:

• zelk12/MT3-Gen3-GP-gemma-2-S4RGDv0.1-9B
• zelk12/MT3-Gen3-MA-gemma-2-S4MT2-9B

The merge was performed using the SLERP method, which involves a spherical linear interpolation between the models. The configuration for the merged model specifies the use of bfloat16 data type, with a parameter t set at 0.25.

Training

The MT3-Gen3-GMA-GEMMA-2-9B was not trained from scratch but rather constructed through the merging of existing pre-trained models using the mergekit library. This approach allows the model to leverage the strengths of multiple models to enhance its performance.

Guide: Running Locally

To run the MT3-Gen3-GMA-GEMMA-2-9B model locally, follow these steps:

1. Install Dependencies: Ensure you have Python installed, along with the Transformers library. You can install it using pip:

   pip install transformers
   

2. Clone the Model Repository: Download the model files from the Hugging Face Model Hub.

3. Load the Model: Use the Transformers library to load the model:

   from transformers import AutoModelForCausalLM, AutoTokenizer
   
   model = AutoModelForCausalLM.from_pretrained("zelk12/MT3-Gen3-GMA-gemma-2-9B")
   tokenizer = AutoTokenizer.from_pretrained("zelk12/MT3-Gen3-GMA-gemma-2-9B")
   

4. Run Inference: Use the model to generate text by passing input strings through it.

For optimal performance, especially when handling large models like this, consider using cloud GPUs such as those provided by AWS, Google Cloud, or Azure.

License

The licensing information for the MT3-Gen3-GMA-GEMMA-2-9B model is available on its Hugging Face model card. Users should ensure compliance with the terms specified there.