Instructions to use premrajreddy/tinyllama-1.1b-home-llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use premrajreddy/tinyllama-1.1b-home-llm with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="premrajreddy/tinyllama-1.1b-home-llm",
	filename="tinyllama-1.1B-merged-F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Inference
Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use premrajreddy/tinyllama-1.1b-home-llm with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf premrajreddy/tinyllama-1.1b-home-llm:F16
# Run inference directly in the terminal:
llama-cli -hf premrajreddy/tinyllama-1.1b-home-llm:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf premrajreddy/tinyllama-1.1b-home-llm:F16
# Run inference directly in the terminal:
llama-cli -hf premrajreddy/tinyllama-1.1b-home-llm:F16

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/tinyllama-1.1b-home-llm:F16
# Run inference directly in the terminal:
./llama-cli -hf premrajreddy/tinyllama-1.1b-home-llm:F16

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/tinyllama-1.1b-home-llm:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf premrajreddy/tinyllama-1.1b-home-llm:F16

Use Docker

docker model run hf.co/premrajreddy/tinyllama-1.1b-home-llm:F16

LM Studio
Jan

vLLM

How to use premrajreddy/tinyllama-1.1b-home-llm with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "premrajreddy/tinyllama-1.1b-home-llm"
# 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/tinyllama-1.1b-home-llm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/premrajreddy/tinyllama-1.1b-home-llm:F16

Ollama
How to use premrajreddy/tinyllama-1.1b-home-llm with Ollama:
```
ollama run hf.co/premrajreddy/tinyllama-1.1b-home-llm:F16
```

Unsloth Studio new

How to use premrajreddy/tinyllama-1.1b-home-llm 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/tinyllama-1.1b-home-llm 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/tinyllama-1.1b-home-llm to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for premrajreddy/tinyllama-1.1b-home-llm to start chatting

Docker Model Runner
How to use premrajreddy/tinyllama-1.1b-home-llm with Docker Model Runner:
```
docker model run hf.co/premrajreddy/tinyllama-1.1b-home-llm:F16
```

Lemonade

How to use premrajreddy/tinyllama-1.1b-home-llm with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull premrajreddy/tinyllama-1.1b-home-llm:F16

Run and chat with the model

lemonade run user.tinyllama-1.1b-home-llm-F16

List all available models

lemonade list

🏠 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))

Downloads last month: 1,307

Safetensors

Model size

1B params

Tensor type

F16

Model tree for premrajreddy/tinyllama-1.1b-home-llm

Base model

TinyLlama/TinyLlama-1.1B-Chat-v1.0

Finetuned

(556)

this model

premrajreddy
/

tinyllama-1.1b-home-llm

🏠 TinyLLaMA-1.1B Home Assistant Voice Model

✨ Features

🔧 Example Usage (Transformers)

Model tree for premrajreddy/tinyllama-1.1b-home-llm

Dataset used to train premrajreddy/tinyllama-1.1b-home-llm