Instructions to use the-robot-ai/tiny-emotion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use the-robot-ai/tiny-emotion with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="the-robot-ai/tiny-emotion",
	filename="tiny-emotion.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use the-robot-ai/tiny-emotion with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf the-robot-ai/tiny-emotion
# Run inference directly in the terminal:
llama-cli -hf the-robot-ai/tiny-emotion

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf the-robot-ai/tiny-emotion
# Run inference directly in the terminal:
llama-cli -hf the-robot-ai/tiny-emotion

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 the-robot-ai/tiny-emotion
# Run inference directly in the terminal:
./llama-cli -hf the-robot-ai/tiny-emotion

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 the-robot-ai/tiny-emotion
# Run inference directly in the terminal:
./build/bin/llama-cli -hf the-robot-ai/tiny-emotion

Use Docker

docker model run hf.co/the-robot-ai/tiny-emotion

LM Studio
Jan
Ollama
How to use the-robot-ai/tiny-emotion with Ollama:
```
ollama run hf.co/the-robot-ai/tiny-emotion
```

Unsloth Studio new

How to use the-robot-ai/tiny-emotion 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 the-robot-ai/tiny-emotion 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 the-robot-ai/tiny-emotion to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for the-robot-ai/tiny-emotion to start chatting

Docker Model Runner
How to use the-robot-ai/tiny-emotion with Docker Model Runner:
```
docker model run hf.co/the-robot-ai/tiny-emotion
```

Lemonade

How to use the-robot-ai/tiny-emotion with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull the-robot-ai/tiny-emotion

Run and chat with the model

lemonade run user.tiny-emotion-{{QUANT_TAG}}

List all available models

lemonade list

Model Summary

tiny-emotion is a lightweight language model fine-tuned to classify emotions in short texts, such as tweets or messages. Designed for speed and efficiency, it can run fully locally, making it ideal for real-time, privacy-preserving applications. The model provides concise, accurate emotion labels, enabling quick insights without unnecessary complexity or lengthy explanations.

Use cases

tiny-emotion is best suited for applications requiring fast, local emotion classification from short-form text. Some potential real-world applications are:

Robotics: Enable robots to better understand and react to human emotions in real time.
Empathetic chatbots: Help virtual assistants respond in a more human, emotionally-aware way.
Mental health tools: Pick up on emotional changes that could signal a shift in someone's well-being.
Customer feedback: Quickly figure out how people feel about your product or service.

Model Behavior

This model keeps things short and clear, in contrast to larger LLMs that may produce long paragraphs or over-explaining. For example:

“Wow, I just won tickets to the concert! Totally unexpected.”

The model outputs:

Surprise

Comparison Example

Model	Output
Tiny-emotion	""Surprise""
ChatGPT	"The emotion expressed is joy or excitement... likely surprise mixed with happiness."
Gemini	"The emotion of the tweet is joy or excitement."

While larger models provide richer explanations, tiny-emotion offers faster, more focused outputs. That makes it super useful for applications where you want quick insights without digging through wordy outputs.

Key Features

Fine-tuned for emotion recognition
Lightweight and fast
Can run locally
Optimized for short texts like tweets, messages, and comments

Downloads last month: 35

Safetensors

Model size

1B params

Tensor type

F16

Video Preview

Robotics

the-robot-ai
/

tiny-emotion

Model Summary

Use cases

Model Behavior

Comparison Example

Key Features

Dataset used to train the-robot-ai/tiny-emotion

Space using the-robot-ai/tiny-emotion 1