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...
###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
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