Kosmos E V A A Franken v38 8 B

Introduction

Kosmos-EVAA-Franken-v38-8B is a merged pre-trained language model created by combining multiple models using the SLERP merge method. This model leverages the capabilities of the transformers library and is designed to facilitate text generation tasks.

Architecture

The model is a result of merging two foundational models: jaspionjader/fct-18-8b and jaspionjader/fct-14-8b. Each model contributes a specific range of layers to the merged model, and the final architecture is configured using a YAML file specifying the model slices and merge method.

Training

This model was not trained from scratch but was instead created by merging pre-existing models using the SLERP method. This method involves interpolating between the parameters of the two base models. The configuration includes specific layer ranges for each model and defines how these layers are combined.

Guide: Running Locally

To run Kosmos-EVAA-Franken-v38-8B locally, follow these steps:

1. Install Dependencies: Ensure you have Python and the transformers library installed.

   pip install transformers
   

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

   from transformers import AutoModelForCausalLM, AutoTokenizer
   
   model_name = "jaspionjader/Kosmos-EVAA-Franken-v38-8B"
   model = AutoModelForCausalLM.from_pretrained(model_name)
   tokenizer = AutoTokenizer.from_pretrained(model_name)
   

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

   input_text = "Your input text here"
   inputs = tokenizer(input_text, return_tensors="pt")
   outputs = model.generate(**inputs)
   print(tokenizer.decode(outputs[0], skip_special_tokens=True))
   

4. Consider Cloud GPUs: For optimal performance, especially with large models, it is recommended to use cloud-based GPUs from providers such as AWS, Google Cloud, or Azure.

License

The license details for Kosmos-EVAA-Franken-v38-8B are not specified in the provided information. Users should refer to the model repository on Hugging Face for complete licensing information.