M T3 Gen5 gemma 2 9 B_v1

Introduction

The MT3-Gen5-gemma-2-9B_v1 is a text generation model available on Hugging Face. It has been created using the mergekit tool, merging pre-trained language models to enhance its capabilities.

Architecture

This model is a result of merging two specific pre-trained models: zelk12/MT3-Gen5-MAI-gemma-2-9B_v1 and zelk12/MT3-Gen5-MMGBMU-gemma-2-9B_v1. The merging process employed the SLERP method, with a configuration set to operate using the bfloat16 data type. The parameter t, which is used in the merging process, is set to 0.25.

Training

The MT3-Gen5-gemma-2-9B_v1 model was created by merging existing pre-trained models using the SLERP method, which is a part of the mergekit tool. This approach allows for the combination of strengths from different models into a unified system.

Guide: Running Locally

1. Installation: Ensure you have Python and the necessary libraries installed, including transformers. You can install them using pip:

   pip install transformers
   

2. Setup: Download the model from Hugging Face's model hub using the transformers library.

3. Execution: Load the model using Python scripts and generate text based on your inputs. Example code snippet:

   from transformers import AutoModelForCausalLM, AutoTokenizer
   
   tokenizer = AutoTokenizer.from_pretrained("zelk12/MT3-Gen5-gemma-2-9B_v1")
   model = AutoModelForCausalLM.from_pretrained("zelk12/MT3-Gen5-gemma-2-9B_v1")
   
   inputs = tokenizer("Your input text here", return_tensors="pt")
   outputs = model.generate(**inputs)
   print(tokenizer.decode(outputs[0], skip_special_tokens=True))
   

4. Hardware: For optimal performance, it is recommended to use cloud GPUs, such as those offered by AWS, Google Cloud, or Azure.

License

The model is released under the gemma license, which dictates the terms and conditions for use, distribution, and modification. Please refer to the license documentation for detailed information.