M T4 Gen5 gemma 2 9 B

Introduction

The MT4-Gen5-GEMMA-2-9B is a merged pre-trained language model designed for text generation. It leverages the capabilities of multiple models using the SLERP merge method, facilitated by the Mergekit tool.

Architecture

This model is a combination of two distinct models:

• zelk12/MT4-Gen5-GMA-gemma-2-9B
• zelk12/MT4-Gen5-IBMUMM-gemma-2-9B

These models are integrated using a YAML configuration that specifies the merge method and parameters. The model operates with a data type of bfloat16.

Training

The MT4-Gen5-GEMMA-2-9B model employs the SLERP merge method for training, which allows for smooth linear interpolation between model parameters. This method ensures a robust integration of the capabilities of the individual models.

Guide: Running Locally

To run the MT4-Gen5-GEMMA-2-9B model locally, follow these steps:

1. Set Up Environment: Ensure you have Python and the necessary libraries installed. You can use a virtual environment for managing dependencies.

   python -m venv env
   source env/bin/activate
   pip install transformers safetensors
   

2. Download the Model: Use the Hugging Face transformers library to download and initialize the model.

   from transformers import AutoModelForCausalLM, AutoTokenizer
   
   model_name = "zelk12/MT4-Gen5-gemma-2-9B"
   model = AutoModelForCausalLM.from_pretrained(model_name)
   tokenizer = AutoTokenizer.from_pretrained(model_name)
   

3. Run Inference: Use the model for text generation.

   input_text = "Once upon a time"
   inputs = tokenizer(input_text, return_tensors="pt")
   outputs = model.generate(**inputs)
   print(tokenizer.decode(outputs[0], skip_special_tokens=True))
   

4. GPU Support: For optimal performance, especially with large models like this, consider using cloud GPUs such as those provided by AWS, Google Cloud, or Azure.

License

The MT4-Gen5-GEMMA-2-9B model is distributed under the gemma license. Consult the license terms for usage rights and restrictions.