Kosmos V E N N 8 B

Introduction

Kosmos-VENN-8B is a merged pre-trained language model designed for text generation tasks. It utilizes the SLERP merge method to combine capabilities from multiple models, resulting in a robust tool for natural language processing.

Architecture

Kosmos-VENN-8B is built by merging two base models: DreadPoor/UNTESTED-VENN_1.2-8B-Model_Stock and Khetterman/Kosmos-8B-v1. The SLERP merge method was employed, which involves interpolating between parameters of the models. The configuration uses a YAML setup specifying layers, merge methods, and parameter filters for different components such as self-attention and multi-layer perceptrons (MLPs), operating in the bfloat16 data type.

Training

The Kosmos-VENN-8B model was not trained from scratch but rather created by merging pre-existing models. The merging process involved selecting specific layers from each base model and adjusting parameters to optimize the resulting model's performance on text generation tasks.

Guide: Running Locally

To run Kosmos-VENN-8B locally, follow these steps:

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

   pip install transformers
   

2. Download the Model: Retrieve the model files from the Hugging Face Model Hub.

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

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

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

4. Consider Cloud GPUs: For optimal performance, especially with large models, using cloud-based GPUs such as those provided by AWS, Google Cloud, or Azure is recommended.

License

The Kosmos-VENN-8B model is released under the licenses of its base models. Users should refer to the model card for specific licensing details and comply with any usage restrictions or guidelines provided.