Instructions to use premrajreddy/Home-TinyLlama-1.1B-HomeAssist-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use premrajreddy/Home-TinyLlama-1.1B-HomeAssist-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="premrajreddy/Home-TinyLlama-1.1B-HomeAssist-GGUF", filename="tinyllama-1.1B-merged-F16.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use premrajreddy/Home-TinyLlama-1.1B-HomeAssist-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf premrajreddy/Home-TinyLlama-1.1B-HomeAssist-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf premrajreddy/Home-TinyLlama-1.1B-HomeAssist-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf premrajreddy/Home-TinyLlama-1.1B-HomeAssist-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf premrajreddy/Home-TinyLlama-1.1B-HomeAssist-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf premrajreddy/Home-TinyLlama-1.1B-HomeAssist-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf premrajreddy/Home-TinyLlama-1.1B-HomeAssist-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf premrajreddy/Home-TinyLlama-1.1B-HomeAssist-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf premrajreddy/Home-TinyLlama-1.1B-HomeAssist-GGUF:Q4_K_M
Use Docker
docker model run hf.co/premrajreddy/Home-TinyLlama-1.1B-HomeAssist-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use premrajreddy/Home-TinyLlama-1.1B-HomeAssist-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "premrajreddy/Home-TinyLlama-1.1B-HomeAssist-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "premrajreddy/Home-TinyLlama-1.1B-HomeAssist-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/premrajreddy/Home-TinyLlama-1.1B-HomeAssist-GGUF:Q4_K_M
- Ollama
How to use premrajreddy/Home-TinyLlama-1.1B-HomeAssist-GGUF with Ollama:
ollama run hf.co/premrajreddy/Home-TinyLlama-1.1B-HomeAssist-GGUF:Q4_K_M
- Unsloth Studio
How to use premrajreddy/Home-TinyLlama-1.1B-HomeAssist-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for premrajreddy/Home-TinyLlama-1.1B-HomeAssist-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for premrajreddy/Home-TinyLlama-1.1B-HomeAssist-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for premrajreddy/Home-TinyLlama-1.1B-HomeAssist-GGUF to start chatting
- Docker Model Runner
How to use premrajreddy/Home-TinyLlama-1.1B-HomeAssist-GGUF with Docker Model Runner:
docker model run hf.co/premrajreddy/Home-TinyLlama-1.1B-HomeAssist-GGUF:Q4_K_M
- Lemonade
How to use premrajreddy/Home-TinyLlama-1.1B-HomeAssist-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull premrajreddy/Home-TinyLlama-1.1B-HomeAssist-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Home-TinyLlama-1.1B-HomeAssist-GGUF-Q4_K_M
List all available models
lemonade list
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, trained with 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)
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))