Instructions to use tiiuae/Falcon-H1-1.5B-Deep-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tiiuae/Falcon-H1-1.5B-Deep-Base with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="tiiuae/Falcon-H1-1.5B-Deep-Base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tiiuae/Falcon-H1-1.5B-Deep-Base")
model = AutoModelForCausalLM.from_pretrained("tiiuae/Falcon-H1-1.5B-Deep-Base")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use tiiuae/Falcon-H1-1.5B-Deep-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tiiuae/Falcon-H1-1.5B-Deep-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tiiuae/Falcon-H1-1.5B-Deep-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/tiiuae/Falcon-H1-1.5B-Deep-Base

SGLang

How to use tiiuae/Falcon-H1-1.5B-Deep-Base with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "tiiuae/Falcon-H1-1.5B-Deep-Base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tiiuae/Falcon-H1-1.5B-Deep-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "tiiuae/Falcon-H1-1.5B-Deep-Base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tiiuae/Falcon-H1-1.5B-Deep-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use tiiuae/Falcon-H1-1.5B-Deep-Base with Docker Model Runner:
```
docker model run hf.co/tiiuae/Falcon-H1-1.5B-Deep-Base
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

TL;DR
Model Details
Training Details
Usage
Evaluation
Citation

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, Multilingual
License: Falcon-LLM License

Training details

For more details about the training protocol of this model, please refer to the Falcon-H1 technical blogpost and Technical Report.

Usage

Currently to use this model you can either rely on Hugging Face transformers, vLLM or llama.cpp library.

Inference

Make sure to install the latest version of transformers or vllm, eventually install these packages from source:

pip install git+https://github.com/huggingface/transformers.git

For vLLM, make sure to install vllm>=0.9.0:

pip install "vllm>=0.9.0"

🤗 transformers

Refer to the snippet below to run H1 models using 🤗 transformers:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "tiiuae/Falcon-H1-1B-Base"

model = AutoModelForCausalLM.from_pretrained(
  model_id,
  torch_dtype=torch.bfloat16,
  device_map="auto"
)

# Perform text generation

vLLM

For vLLM, simply start a server by executing the command below:

# pip install vllm>=0.9.0
vllm serve tiiuae/Falcon-H1-1B-Instruct --tensor-parallel-size 2 --data-parallel-size 1

`llama.cpp`

You can find all GGUF files compatible with llama.cpp under our official collection

Evaluation

Falcon-H1 series perform very well on a variety of tasks, including reasoning tasks.

Tasks	Falcon-H1-1.5B-deep	Qwen3-1.7B	Qwen2.5-1.5B	Gemma3-1B	Llama3.2-1B	Falcon3-1B
General
BBH	52.37	43.05	40.55	30.26	30.72	35.24
MMLU	66.29	62.46	61.13	26.33	32.39	45.14
ARC-C	55.89	55.72	54.27	39.33	39.42	47.87
HellaSwag	69.72	67.09	67.86	62.94	65.73	62.3
Winogrande	67.09	66.3	64.56	62.59	62.75	61.17
Math
GSM8k	68.69	70.74	63.0	2.2	7.05	34.95
MATH lvl5	24.77	16.39	8.84	1.21	0.98	3.4
Science
GPQA	32.8	29.45	28.36	24.66	23.57	27.85
MMLU-Pro	41.07	33.81	28.72	11.31	11.8	16.11
MMLU-stem	67.43	61.53	54.93	27.59	30.19	40.06
Code
HumanEval	52.44	67.68	35.37	6.71	18.9	10.37
HumanEval+	46.34	60.98	29.27	5.49	16.46	9.15
MBPP	70.9	67.72	60.05	12.7	35.98	12.43
MBPP+	60.32	58.99	49.47	9.52	29.89	9.52

You can check more in detail on our our release blogpost, detailed benchmarks.

Useful links

View our release blogpost.
View our technical report.
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 family of models were helpful to your work, feel free to give us a cite.

@article{falconh1,
    title={Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance},
    author={Jingwei Zuo and Maksim Velikanov and Ilyas Chahed and Younes Belkada and Dhia Eddine Rhayem and Guillaume Kunsch and Hakim Hacid and Hamza Yous and Brahim Farhat and Ibrahim Khadraoui and Mugariya Farooq and Giulia Campesan and Ruxandra Cojocaru and Yasser Djilali and Shi Hu and Iheb Chaabane and Puneesh Khanna and Mohamed El Amine Seddik and Ngoc Dung Huynh and Phuc Le Khac and Leen AlQadi and Billel Mokeddem and Mohamed Chami and Abdalgader Abubaker and Mikhail Lubinets and Kacper Piskorski and Slim Frikha},
    journal = {arXiv preprint arXiv:2507.22448},
    year={2025}
}