Twin Llama 3.1 8 B

Introduction

TwinLlama-3.1-8B is a language model developed as part of the LLM Engineer's Handbook. It functions as a digital twin, closely mimicking the writing style and knowledge base of its creators, Mlabonne, Paul Iusztin, and Alex Vesa. This model is particularly tailored for text generation tasks.

Architecture

TwinLlama-3.1-8B is based on the Meta-Llama architecture, specifically the Meta-Llama-3.1-8B variant. It leverages the Transformers library for its implementation and supports text generation in English. The model is also compatible with Safetensors, ensuring secure tensor data management.

Training

The model was trained using the mlabonne/llmtwin dataset, which is designed to capture the unique writing styles of the authors. The training process was accelerated using Unsloth and Hugging Face's TRL library, resulting in a training speed twice as fast as traditional methods.

Guide: Running Locally

1. Setup Environment
   Install Python and necessary libraries:

   pip install transformers
   

2. Download Model
   Clone the model repository and download the weights:

   git clone https://huggingface.co/mlabonne/TwinLlama-3.1-8B
   

3. Load and Run Model
   Utilize the Transformers library to load and test the model:

   from transformers import AutoModelForCausalLM, AutoTokenizer
   
   tokenizer = AutoTokenizer.from_pretrained("mlabonne/TwinLlama-3.1-8B")
   model = AutoModelForCausalLM.from_pretrained("mlabonne/TwinLlama-3.1-8B")
   
   input_text = "Your input here."
   inputs = tokenizer(input_text, return_tensors="pt")
   outputs = model.generate(**inputs)
   print(tokenizer.decode(outputs[0]))
   

4. Cloud GPUs
   For optimal performance, especially for large-scale tasks, consider using cloud-based GPUs such as AWS EC2, Google Cloud, or Azure.

License

TwinLlama-3.1-8B is distributed under the Apache-2.0 license, allowing for broad use and modification with proper attribution.