Llama 3.2 instruct fintuned

Introduction

The Llama-3.2-Instruct-Fintuned model is a fine-tuned version of the Meta Llama-3.2-3B-Instruct model, designed for improved performance on specific tasks. It utilizes the Transformers library and leverages datasets such as Karthiksiva/therapist_bot_data to enhance its capabilities.

Architecture

The model is built upon the Meta Llama-3.2 architecture, specifically the 3 billion parameter instruct variant. This architecture is part of the larger Llama series known for its efficiency and robustness in natural language processing tasks.

Training

The model was fine-tuned using the Karthiksiva/therapist_bot_data dataset. The fine-tuning process aims to adapt the pre-trained model to perform better on tasks that require conversational or instructional outputs, enhancing its effectiveness in real-world applications.

Guide: Running Locally

To run the Llama-3.2-Instruct-Fintuned model locally, follow these steps:

1. Install the Transformers library:

   pip install transformers
   

2. Download the model:

   • Access the model repository and download the necessary files.

3. Load the model in your script:

   from transformers import AutoModelForCausalLM, AutoTokenizer
   
   tokenizer = AutoTokenizer.from_pretrained("aishfi/Llama-3.2-instruct-fintuned")
   model = AutoModelForCausalLM.from_pretrained("aishfi/Llama-3.2-instruct-fintuned")
   

4. Run inference:

   • Prepare input data and use the model to generate outputs.

For optimal performance, especially with large models, consider using cloud GPUs such as those available on AWS, Google Cloud, or Azure.

License

The licensing details for the Llama-3.2-Instruct-Fintuned model are not explicitly provided. Please refer to the model's repository or contact the author for specific licensing information.