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Finetuning Overview:

Model Used: gpt2

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 58mins 48s for 1 epochs using an A6000 48GB GPU.
  • Costed $1.96 for the entire 1 epoch.

Hyperparameters & Additional Details:

  • Epochs: 1
  • Total Finetuning Cost: $1.96
  • Model Path: gpt2
  • Learning Rate: 0.0002
  • Data Split: 100% train
  • Gradient Accumulation Steps: 128
  • lora r: 32
  • lora alpha: 64

Train Loss


license: apache-2.0

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