Instructions to use tiiuae/Falcon-H1-Tiny-R-0.6B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiiuae/Falcon-H1-Tiny-R-0.6B-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("tiiuae/Falcon-H1-Tiny-R-0.6B-GGUF", dtype="auto") - llama-cpp-python
How to use tiiuae/Falcon-H1-Tiny-R-0.6B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="tiiuae/Falcon-H1-Tiny-R-0.6B-GGUF", filename="Falcon-H1R-0.6B-BF16.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 tiiuae/Falcon-H1-Tiny-R-0.6B-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf tiiuae/Falcon-H1-Tiny-R-0.6B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf tiiuae/Falcon-H1-Tiny-R-0.6B-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 tiiuae/Falcon-H1-Tiny-R-0.6B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf tiiuae/Falcon-H1-Tiny-R-0.6B-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 tiiuae/Falcon-H1-Tiny-R-0.6B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf tiiuae/Falcon-H1-Tiny-R-0.6B-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 tiiuae/Falcon-H1-Tiny-R-0.6B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf tiiuae/Falcon-H1-Tiny-R-0.6B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/tiiuae/Falcon-H1-Tiny-R-0.6B-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use tiiuae/Falcon-H1-Tiny-R-0.6B-GGUF with Ollama:
ollama run hf.co/tiiuae/Falcon-H1-Tiny-R-0.6B-GGUF:Q4_K_M
- Unsloth Studio new
How to use tiiuae/Falcon-H1-Tiny-R-0.6B-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 tiiuae/Falcon-H1-Tiny-R-0.6B-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 tiiuae/Falcon-H1-Tiny-R-0.6B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for tiiuae/Falcon-H1-Tiny-R-0.6B-GGUF to start chatting
- Pi new
How to use tiiuae/Falcon-H1-Tiny-R-0.6B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf tiiuae/Falcon-H1-Tiny-R-0.6B-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "tiiuae/Falcon-H1-Tiny-R-0.6B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use tiiuae/Falcon-H1-Tiny-R-0.6B-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf tiiuae/Falcon-H1-Tiny-R-0.6B-GGUF:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default tiiuae/Falcon-H1-Tiny-R-0.6B-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use tiiuae/Falcon-H1-Tiny-R-0.6B-GGUF with Docker Model Runner:
docker model run hf.co/tiiuae/Falcon-H1-Tiny-R-0.6B-GGUF:Q4_K_M
- Lemonade
How to use tiiuae/Falcon-H1-Tiny-R-0.6B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tiiuae/Falcon-H1-Tiny-R-0.6B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Falcon-H1-Tiny-R-0.6B-GGUF-Q4_K_M
List all available models
lemonade list
Table of Contents
TL;DR
Model Details
Model Description
- Developed by: https://www.tii.ae
- Model type: Causal decoder-only
- Architecture: Hybrid Transformers + Mamba architecture
- Language(s) (NLP): English
- Number of Parameters: 90M
- License: Falcon-LLM License
Training details
For more details about the training protocol of this model, please refer to the Falcon-H1-Tiny technical blogpost.
Usage
Currently to use this model you can either rely on Hugging Face transformers, vLLM, sglang, llama.cpp, ollama or mlx library.
Inference
llama.cpp
You can find all GGUF files compatible with llama.cpp under our official collection - an example setup could be:
brew install llama.cpp
pip install huggingface_hub
hf download tiiuae/Falcon-H1-Tiny-R-0.6B-GGUF Falcon-H1-Tiny-R-0.6B-GGUF-Q8_0.gguf --local-dir ./
llama-cli ./Falcon-H1-Tiny-R-0.6B-GGUF-Q8_0.gguf -cnv
ollama
ollama run hf.co/tiiuae/Falcon-H1-Tiny-R-0.6B-GGUF:Q8_0
Evaluation
For detailed evaluation of Falcon-H1-Tiny series, please refer to our technical blogpost
Useful links
- View our release blogpost.
- Feel free to join our discord server if you have any questions or to interact with our researchers and developers.
Citation
If the Falcon-H1-Tiny family of models were helpful to your work, feel free to give us a cite.
@misc{falcon_h1_tiny,
title={Falcon-H1-Tiny: A series of extremely small, yet powerful language models redefining capabilities at small scale},
author={Falcon-LLM Team},
year={2026},
}
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