The "Home" model is a fine tuning of the FunctionGemma model from Google. The model is able to control devices in the user's house via the "Assist" API, as well as perform basic question answering about the provided home's state.

The model is quantized using Lama.cpp in order to enable running the model in super low resource environments that are common with Home Assistant installations such as Rapsberry Pis.

Training

Datasets

Home Assistant Requests V2 - https://huggingface.co/datasets/acon96/Home-Assistant-Requests-V2

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 16
total_train_batch_size: 32
total_eval_batch_size: 2
optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 59
training_steps: 597

License

The model is licensed under the Gemma license as it is a fine-tuning of the FunctionGemma model.

onnx-community/Home-FunctionGemma-270m-ONNX

Home-FunctionGemma-270m (ONNX)

Usage with Transformers.js

Home-FunctionGemma-270m

Training

Datasets

Training hyperparameters

License

onnx-community/
Home-FunctionGemma-270m-ONNX