base_model: unsloth/llama-2-7b-chat-bnb-4bit
datasets:
- piotr25691/ultrachat-200k-alpaca
language:
- en
library_name: peft
license: apache-2.0
pipeline_tag: text-generation
Xander
Welcome to the Xander Conversational Model repository! This model has been fine-tuned from the unsloth/llama-2-7b-chat-bnb-4bit base on the piotr25691/ultrachat-200k-alpaca dataset to enhance its conversational abilities. It is designed to provide more natural, engaging, and contextually aware responses.
Introduction
The Xander Conversational Model is an advanced NLP model aimed at improving interactive text generation. By leveraging the strengths of unsloth/llama-2-7b-chat-bnb-4bit and fine-tuning it with the extensive piotr25691/ultrachat-200k-alpaca dataset, the model is adept at generating coherent and contextually relevant conversations.
Features
- Improved Conversational Flow: Generates more natural and engaging responses.
- Context Awareness: Maintains context over multiple interactions.
- Customizable: Can be further fine-tuned for specific applications or industries.
Dataset
The model was fine-tuned on the piotr25691/ultrachat-200k-alpaca dataset, which consists of 200,000 high-quality conversational pairs. This dataset helps the model to understand and generate more nuanced and contextually appropriate responses.
Performance
The model has shown significant improvements in generating more human-like responses compared to its base. Here are some key metrics:
- Perplexity: Lower perplexity indicating better language modeling performance.
- Response Coherence: Improved coherence in multi-turn conversations.
- Engagement: Higher user satisfaction in interactive scenarios.