monsterapi/gemma-2b-lora-maths-orca-200k

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

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license: apache-2.0