metadata
library_name: peft
tags:
- code
- instruct
- llama2
datasets:
- cognitivecomputations/dolphin-coder
base_model: meta-llama/Llama-2-7b-hf
license: apache-2.0
Finetuning Overview:
Model Used: meta-llama/Llama-2-7b-hf
Dataset: cognitivecomputations/dolphin-coder
Dataset Insights:
Dolphin-Coder dataset – a high-quality collection of 100,000+ coding questions and responses. It's perfect for supervised fine-tuning (SFT), and teaching language models to improve on coding-based tasks.
Finetuning Details:
With the utilization of MonsterAPI's no-code LLM finetuner, this finetuning:
- Was achieved with great cost-effectiveness.
- Completed in a total duration of 15hr 31mins for 1 epochs using an A6000 48GB GPU.
- Costed
$30.64for the entire 1 epoch.
Hyperparameters & Additional Details:
- Epochs: 1
- Total Finetuning Cost: $30.64
- Model Path: meta-llama/Llama-2-7b-hf
- Learning Rate: 0.0002
- Data Split: 100% train
- Gradient Accumulation Steps: 128
- lora r: 32
- lora alpha: 64
license: apache-2.0