| | --- |
| | base_model: microsoft/Phi-3-mini-4k-instruct |
| | datasets: |
| | - AlignmentLab-AI/alpaca-cot-collection |
| | language: |
| | - en |
| | library_name: peft |
| | license: apache-2.0 |
| | pipeline_tag: text-generation |
| | --- |
| | |
| | # Xenith-3B |
| | 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. |
