Llama 3.2 3 B Sci Think

Introduction

The LLAMA-3.2-3B-SCI-THINK model is a pre-trained language model designed for text generation tasks. It leverages advanced techniques for merging models to enhance performance, particularly in scientific thinking contexts.

Architecture

LLAMA-3.2-3B-SCI-THINK is built on the transformers library. The architecture involves a merger of two distinct pre-trained models: huihui-ai/Llama-3.2-3B-Instruct-abliterated and bunnycore/Llama-3.2-3B-science-lora_model. The merging process uses a "passthrough" method facilitated by the mergekit tool.

Training

The model combines the capabilities of its constituent models using a specific YAML configuration. This configuration allows the model to maintain the strengths of both base models, providing a robust foundation for generating scientifically oriented text.

Guide: Running Locally

To run the LLAMA-3.2-3B-SCI-THINK model locally, follow these steps:

1. Install Dependencies: Ensure you have Python and the transformers library installed. You may also need to install the mergekit tool if further model adjustments are required.

   pip install transformers
   pip install mergekit
   

2. Clone the Model Repository: Access the model repository on Hugging Face and clone it to your local environment.

   git clone https://huggingface.co/bunnycore/Llama-3.2-3B-Sci-Think
   

3. Load the Model: Use the transformers library to load and initialize the model in your script.

   from transformers import AutoModelForCausalLM, AutoTokenizer
   
   tokenizer = AutoTokenizer.from_pretrained("bunnycore/Llama-3.2-3B-Sci-Think")
   model = AutoModelForCausalLM.from_pretrained("bunnycore/Llama-3.2-3B-Sci-Think")
   

4. Run Inference: Implement text generation by feeding input sequences to the model.

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

5. Consider Using Cloud GPUs: For efficient performance, especially when handling large datasets or complex computations, consider using cloud-based GPU services such as AWS EC2, Google Cloud Platform, or Azure.

License

The license details for the LLAMA-3.2-3B-SCI-THINK model have not been explicitly provided. Users should refer to the Hugging Face repository for any specific licensing terms and conditions.