Kosmos E V A A mix v35 8 B

Introduction

Kosmos-EVAA-mix-v35-8B is a text generation model resulting from the merging of pre-trained language models using the Mergekit tool. It employs a specific merging strategy to enhance the capabilities of the base models.

Architecture

The model architecture is based on a combination of two models: jaspionjader/test-19 and jaspionjader/test-18. The merging process utilizes the SLERP (Spherical Linear Interpolation) method to integrate layers from these models effectively.

Training

The model was constructed by merging pre-trained models using the following configuration:

• Models Merged: jaspionjader/test-19 and jaspionjader/test-18.
• Layer Ranges: Layers 0 to 32 from both models were used.
• Merge Method: SLERP, which involves interpolating weights between layers.
• Parameters:
  • Self-attention layers have weights interpolated in a range from 0 to 1.
  • MLP layers have a different set of interpolated values.
• Data Type: The model uses bfloat16 for computation.

Guide: Running Locally

1. Setup Environment:

   • Ensure you have Python installed.
   • Install the transformers library via pip:

     pip install transformers
     

2. Download the Model:

   • Use the Hugging Face transformers library to load the model:

     from transformers import AutoModelForCausalLM, AutoTokenizer
     
     model = AutoModelForCausalLM.from_pretrained("jaspionjader/Kosmos-EVAA-mix-v35-8B")
     tokenizer = AutoTokenizer.from_pretrained("jaspionjader/Kosmos-EVAA-mix-v35-8B")
     

3. Run Inference:

   • Use the tokenizer and model to generate text:

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

4. Hardware Suggestions:

   • For efficient computation, especially with large models, consider using cloud services with GPU support such as AWS, GCP, or Azure.

License

The model and its associated files are subject to the licensing terms provided by the creator, which should be reviewed to ensure compliance with use-case scenarios.