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---
license: other
license_name: innovatronix-restricted-license
license_link: LICENSE
language:
- en
library_name: transformers
---
# 🏠 Innovatronix Home Automation Language Model (Beta)
This model, created by the Innovatronix team, serves as a lightweight LLM model tailored for home automation applications. It is a fine-tuned version of Flan T5, trained on a custom dataset containing data related to home automation. The model is designed for basic conversational interactions and is currently in beta development.
## ✨ Features
- **Conversational Control:** Engage in dialogue with the model to automate smart home functions, commanding and retrieving data from connected smart devices through natural language interactions.
- **Lightweight and Efficient:** Optimized for reduced storage and computational demands, allowing seamless deployment in local environments without excessive resource consumption.
- **Versatile Deployment:** Flexibly deployable across various platforms, including mobile applications and web interfaces, providing users with accessibility to control their smart homes from preferred devices.
## 📊 Training Data
The model was fine-tuned on a meticulously handcrafted dataset encompassing a wide range of commands, queries, and contextual information pertaining to controlling and managing smart devices within a home setting.
You can view and download the dataset here: [Dataset](https://drive.google.com/file/d/1O8UvuiTia1SZGHLHdFZ8lHeaeMdeoobw/view?usp=sharing)
## ⭐ Example usage
```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Robin246/inxai_v1.1")
model = AutoModelForSeq2SeqLM.from_pretrained("Robin246/inxai_v1.1")
# Adjust the parameters if needed
def generate_response(input_prompt, model, tokenizer):
input_text = f"Input prompt: {input_prompt}"
input_ids = tokenizer.encode(input_text, return_tensors="pt", max_length=64, padding="max_length", truncation=True)
output_ids = model.generate(input_ids,
max_length=256,
num_return_sequences=1,
num_beams=2,
early_stopping=True,
#do_sample=True,
#temperature=0.8,
#top_k=50
) #You can vary the top_k or add any other parameters
generated_output = tokenizer.decode(output_ids[0], skip_special_tokens=True)
return generated_output
while True:
user_input = input("Enter prompt: ")
user_input = ["{}".format(user_input)]
if user_input=='Quit':
break
else:
reply = generate_response(user_input, model, tokenizer)
print("Generated Reply({}):".format(model), reply)
#INXAI from huggingface 'Robin246/inxai_v1.1'
```
### ✎ Citation
**Developers:**
- Robinkumar
- Kiransekar
- Magesh
- Lathikaa Shri
<!-- - Nithishkumar -->
**Base model credits**
This model was fine-tuned from Google's Flan-T5 Model using a custom dataset. |