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AliRGHZ/FunctionGemma-270M-it-Mobile-Actions-Combined

sourceHugging Facegemmaupdated 29d agoView on Hugging Face

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Function Gemma

Function Gemma is a Google trained light-wight model with 270M parameters to enables users to make connection between their prompt and executive actions. You can find another variant of the model fine-tuned by Google over (google/mobile-actions)[https://huggingface.co/datasets/google/mobile-actions] here: litert-community/FunctionGemma_270M_Mobile_Actions. You can also use this FunctionGemma-270m-it-Extended-Mobile-Actions model variation fine-tuned over extended Mobile Actions tools set dataset in AliRGHZ/Mobile-Actions.

For deploying the model into your application, kindly follow the instructions available here. For further fine-tuning you can follow the instructions suggested by Google...

Notice: Gemma is provided under and subject to the Gemma Terms of Use found at "ai.google.dev/gemma/terms".

###Training Configuration:

—Epochs: 2
—Batch size: 4 per device
—Gradient accumulation steps: 8
—Learning rate: 1e-5
—Scheduler: Cosine
—Optimizer: AdamW (fused)
—Precision: bfloat16
—Gradient checkpointing: Enabled
—Completion only loss: True (trains only on model outputs, not prompts)

###Training Infrastructure: Hardware: Google Colab A100 GPU Training time: ~24 minutes for 2 epochs Library versions: transformers==5.2.0 torch==2.10.0 huggingface_hub==1.5.0, trl==0.29.0, accelerate==1.13.0