Overview

Lili85/Llama2-7BCoQA-full is a 7 billion parameter language model derived from the meta-llama/Llama-2-7b-hf base model. It has undergone supervised fine-tuning (SFT) using the TRL framework, specifically targeting conversational question answering (CoQA) tasks. This fine-tuning process aims to enhance its ability to understand and generate responses within a dialogue context.

Key Capabilities

• Conversational Question Answering: Optimized for generating relevant answers in multi-turn conversations.
• Llama-2 Architecture: Benefits from the robust architecture of the Llama-2 family.
• TRL Framework: Utilizes the Transformer Reinforcement Learning (TRL) library for its training methodology.

Training Details

The model was trained with SFT, leveraging TRL version 0.25.1, Transformers 4.57.3, Pytorch 2.8.0+cu128, Datasets 3.6.0, and Tokenizers 0.22.1. The training process was tracked and can be visualized via Weights & Biases, indicating a structured approach to its development.

Good For

• Applications requiring a Llama-2 based model with enhanced conversational abilities.
• Developing chatbots or virtual assistants that need to maintain context across multiple turns.
• Research into supervised fine-tuning techniques for dialogue systems.