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 under our official collection

Evaluation

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

Tasks	Falcon-H1-34B	Qwen3-32B	Qwen2.5-72B	Qwen2.5-32B	Gemma3-27B	Llama3.3-70B	Llama4-scout
General
BBH	70.68	62.47	72.52	68.72	67.28	69.15	64.9
ARC-C	61.01	48.98	46.59	44.54	54.52	63.65	56.14
TruthfulQA	65.27	58.58	69.8	70.28	64.26	66.15	62.74
HellaSwag	81.94	68.89	68.79	73.95	57.25	70.24	65.03
MMLU	84.05	80.89	84.42	82.8	78.01	82.08	80.4
Math
GSM8k	83.62	88.78	82.26	78.47	90.37	93.71	90.37
MATH-500	83.8	82.0	83.6	82.2	90.0	70.6	83.2
AMC-23	69.38	67.34	67.34	68.75	77.81	39.38	69.06
AIME-24	23.75	27.71	17.29	17.92	27.5	12.92	27.92
AIME-25	16.67	19.79	15.21	11.46	22.71	1.25	8.96
Science
GPQA	41.53	30.2	37.67	34.31	36.49	31.99	31.8
GPQA_Diamond	49.66	49.49	44.95	40.74	47.47	42.09	51.18
MMLU-Pro	58.73	54.68	56.35	56.63	47.81	53.29	55.58
MMLU-stem	83.57	81.64	82.59	82.37	73.55	74.88	75.2
Code
HumanEval	87.2	90.85	87.2	90.24	86.59	83.53	85.4
HumanEval+	81.71	85.37	80.49	82.32	78.05	79.87	78.7
MBPP	83.86	86.24	89.68	87.83	88.36	88.09	81.5
MBPP+	71.43	71.96	75.4	74.07	74.07	73.81	64.8
LiveCodeBench	49.71	45.01	54.6	49.12	39.53	40.31	40.12
CRUXEval	73.07	78.45	75.63	73.5	74.82	69.53	68.32
Instruction Following
IFEval	89.37	86.97	86.35	81.79	83.19	89.94	86.32
Alpaca-Eval	48.32	64.21	49.29	39.26	56.16	38.27	36.26
MTBench	9.2	9.05	9.16	9.09	8.75	8.98	8.98
LiveBench	46.26	63.05	54.03	52.92	55.41	53.11	54.21

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}
}

tiiuae/Falcon-H1-34B-Instruct

Table of Contents

TL;DR

Model Details

Model Description

Training details

Usage

Inference

🤗 transformers

vLLM

llama.cpp

Evaluation

Useful links

Citation

tiiuae/
Falcon-H1-34B-Instruct

`llama.cpp`