File size: 1,503 Bytes
ca36808 5f59c4a ca36808 463ce72 ca36808 463ce72 5f59c4a ca36808 463ce72 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 |
---
language: en
license: apache-2.0
tags:
- home-assistant
- voice-assistant
- automation
- assistant
- home
pipeline_tag: text-generation
datasets:
- acon96/Home-Assistant-Requests
base_model:
- TinyLlama/TinyLlama-1.1B-Chat-v1.0
base_model_relation: finetune
---
# 🏠 TinyLLaMA-1.1B Home Assistant Voice Model
This model is a **fine-tuned version** of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0), trained with [acon96/Home-Assistant-Requests](https://huggingface.co/datasets/acon96/Home-Assistant-Requests).
It is designed to act as a **voice-controlled smart home assistant** that takes natural language instructions and outputs **Home Assistant commands**.
---
## ✨ Features
- Converts **natural language voice commands** into Home Assistant automation calls.
- Produces **friendly confirmations** and **structured JSON service commands**.
- Lightweight (1.1B parameters) – runs efficiently on CPUs, GPUs, and via **Ollama** with quantization.
---
## 🔧 Example Usage (Transformers)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("premrajreddy/tinyllama-1.1b-home-llm")
model = AutoModelForCausalLM.from_pretrained("premrajreddy/tinyllama-1.1b-home-llm")
query = "turn on the kitchen lights"
inputs = tokenizer(query, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=80)
print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |