Model Overview

ez_hf2024/Llama-3_2-ft is a 1 billion parameter instruction-tuned language model, built upon the meta-llama/Llama-3.2-1B-Instruct base. This model has undergone supervised fine-tuning (SFT) using the TRL library, a framework specifically designed for Transformer Reinforcement Learning. The fine-tuning process aims to enhance the model's ability to follow instructions and generate coherent, contextually relevant responses.

Key Capabilities

• Instruction Following: Optimized for understanding and executing user instructions effectively.
• Text Generation: Capable of generating human-like text based on prompts.
• Compact Size: At 1 billion parameters, it offers a balance between performance and computational efficiency, making it suitable for resource-constrained environments or applications requiring faster inference.
• Extended Context: Supports a context length of 32768 tokens, allowing for processing and generating longer sequences of text.

Training Details

The model was trained using the SFT method within the TRL framework. The development utilized specific versions of key libraries:

• TRL: 0.14.0
• Transformers: 4.48.2
• Pytorch: 2.6.0
• Datasets: 3.2.0
• Tokenizers: 0.21.0

This fine-tuned model is well-suited for applications where a smaller, instruction-tuned language model with good performance on conversational tasks is required.