Lili85/Llama2-7BCoQA-full
Lili85/Llama2-7BCoQA-full is a 7 billion parameter Llama-2-based language model, fine-tuned from meta-llama/Llama-2-7b-hf. This model is specifically trained for conversational question answering (CoQA) tasks, leveraging the TRL framework for supervised fine-tuning. It is designed to generate coherent and contextually relevant responses in interactive dialogue settings, making it suitable for applications requiring nuanced conversational understanding.
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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.