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Finetuning Overview:

Model Used: google/gemma-2b

Dataset: microsoft/orca-math-word-problems-200k

Dataset Insights:

Math Ocra - This dataset contains ~200K grade school math word problems. All the answers in this dataset is generated using Azure GPT4-Turbo. Please refer to Orca-Math: Unlocking the potential of SLMs in Grade School Math for details about the dataset construction.

Finetuning Details:

With the utilization of MonsterAPI's no-code LLM finetuner, this finetuning:

  • A remarkable 68% boost in performance over the base model.
  • Completed in a total duration of 2d 7h 45m for 10 epochs using an A6000 48GB GPU.
  • Demonstrated cost-effectiveness, with a single epoch costing only $11.3.


Hyperparameters & Additional Details:

  • Epochs: 10
  • Total Finetuning Cost: $113
  • Model Path: google/gemma-2b
  • Learning Rate: 0.0001
  • Gradient Accumulation Steps: 32
  • lora_alpha: 128
  • lora_r: 64

Benchmarking Performance Details:

Finetuned Gemma-2B using MonsterAPI achieved a remarkable score of 20.02 on the GSM Plus benchmark.

  • This represents a 68% improvement over its base model performance.
  • Notably, it outperformed larger models like LLaMA-2-13B and Code-LLaMA-7B This result suggests that targeted fine-tuning can significantly improve model performance.

Read the Detailed Case Study over here

Benchmarking Performance

license: apache-2.0

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Dataset used to train monsterapi/gemma-2b-lora-maths-orca-200k