Open LLM List
Kosmos E V A A Fusion light 8 B
jaspionjaderIntroduction
The Kosmos-EVAA-Fusion-Light-8B model is a merged pre-trained language model that leverages the capabilities of multiple models using the mergekit tool. This model is designed to enhance text generation tasks by combining features from different pre-trained models.
Architecture
The Kosmos-EVAA-Fusion-Light-8B model is constructed by merging two base models: Kosmos-EVAA-Fusion-8B and Kosmos-EVAA-v3-8B. The SLERP (Spherical Linear Interpolation) merge method was employed to integrate these models. The model configuration uses layers 0 through 32 from both base models, with parameters specifically tuned for self-attention and MLP filters.
Training
The model was created using the SLERP method, which involves interpolating between the layers of the two base models. The merging process utilized a YAML configuration that defined the layer ranges and parameters for the self-attention and MLP filters. The final model uses a bfloat16 data type for improved computational efficiency.
Guide: Running Locally
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Clone the Repository: Use Git to clone the model repository to your local machine.
git clone https://huggingface.co/jaspionjader/Kosmos-EVAA-Fusion-light-8B -
Set Up Environment: Install the necessary dependencies, including the Hugging Face Transformers library.
pip install transformers -
Load the Model: Use the Transformers library to load and initialize the model.
from transformers import AutoModel model = AutoModel.from_pretrained("jaspionjader/Kosmos-EVAA-Fusion-light-8B") -
Run Inference: Utilize the model for text generation tasks as required by your application.
Consider using cloud GPU services like AWS, GCP, or Azure for enhanced performance, especially when working with large models such as this one.
License
The Kosmos-EVAA-Fusion-Light-8B model is released under the terms specified by the creators on the Hugging Face platform. Users are encouraged to review the model's license for any usage restrictions or conditions.
