Xenith-3B is a fine-tuned language model based on the microsoft/Phi-3-mini-4k-instruct model. It has been specifically trained on the AlignmentLab-AI/alpaca-cot-collection dataset, which focuses on chain-of-thought reasoning and instruction following.
Model Overview
Model Name: Xenith-3B
Base Model: microsoft/Phi-3-mini-4k-instruct
Fine-Tuned On: AlignmentLab-AI/alpaca-cot-collection
Model Size: 3 Billion parameters
Architecture: Transformer-based LLM
Training Details
Objective: Fine-tune the base model to enhance its performance on tasks requiring complex reasoning and multi-step problem-solving.
Training Duration: 10 epochs
Batch Size: 8
Learning Rate: 3e-5
Optimizer: AdamW
Hardware Used: 2x NVIDIA L4 GPUs
Performance
Xenith-3B excels in tasks that require:
Chain-of-thought reasoning
Instruction following
Contextual understanding
Complex problem-solving
The model has shown significant improvements in these areas compared to the base model.
