Kosmos-EVAA-Gamma-Light-8B

Introduction

Kosmos-EVAA-Gamma-Light-8B is a text generation model that results from merging two pre-trained language models: Kosmos-EVAA-gamma-8B and Kosmos-EVAA-v12-8B. This model is designed for efficient text generation and utilizes the transformers library.

Architecture

The model was created using the SLERP merge method, combining specific layers from the two source models. The architecture leverages the strengths of both models, incorporating a blend of self-attention and multi-layer perceptron (MLP) components with specific parameter settings for optimized performance.

Training

The model was not trained from scratch but developed by merging two existing models:

• Kosmos-EVAA-gamma-8B
• Kosmos-EVAA-v12-8B

The merging process used the SLERP method, focusing on layers 0 to 32 from each model. This approach ensures a balanced integration of features from both models.

Guide: Running Locally

To run Kosmos-EVAA-Gamma-Light-8B locally, follow these steps:

1. Clone the Repository:

   git clone https://huggingface.co/jaspionjader/Kosmos-EVAA-gamma-light-8B
   cd Kosmos-EVAA-gamma-light-8B
   

2. Install Dependencies: Ensure you have Python and transformers library installed. You can do this via pip:

   pip install transformers
   

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

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

4. Inference: Generate text by feeding input text to the model.

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

5. Hardware Recommendations: For optimal performance, consider using cloud GPUs such as those available via AWS, Google Cloud, or Azure to handle the computational demands of the model.

License

This model and its components are provided under the Hugging Face model license. Please review the license for detailed usage rights and restrictions.