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
vllm serve tiiuae/Falcon-H1-1B-Instruct --tensor-parallel-size 2 --data-parallel-size 1

`llama.cpp`

While we are working on integrating our architecture directly into llama.cpp library, you can install our fork of the library and use it directly: https://github.com/tiiuae/llama.cpp-Falcon-H1 Use the same installing guidelines as llama.cpp.

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	54.43	35.18	42.41	35.86	33.21	34.47
ARC-C	43.86	34.81	40.53	34.13	34.64	43.09
TruthfulQA	50.48	49.39	47.05	42.17	42.08	42.31
HellaSwag	65.54	49.27	62.23	42.24	55.3	58.53
MMLU	66.11	57.04	59.76	40.87	45.93	46.1
Math
GSM8k	82.34	69.83	57.47	42.38	44.28	44.05
MATH-500	77.8	73.0	48.4	45.4	13.2	19.8
AMC-23	56.56	46.09	24.06	19.22	7.19	6.87
AIME-24	14.37	12.5	2.29	0.42	1.46	0.41
AIME-25	11.04	8.12	1.25	1.25	0.0	0.21
Science
GPQA	33.22	27.68	26.26	28.19	26.59	26.76
GPQA_Diamond	40.57	33.33	25.59	21.55	25.08	31.31
MMLU-Pro	41.89	23.54	28.35	14.46	16.2	18.49
MMLU-stem	67.3	54.3	54.04	35.39	39.16	39.64
Code
HumanEval	73.78	67.68	56.1	40.85	34.15	22.56
HumanEval+	68.9	60.96	50.61	37.2	29.88	20.73
MBPP	68.25	58.73	64.81	57.67	33.6	20.63
MBPP+	56.61	49.74	56.08	50.0	29.37	17.2
LiveCodeBench	23.87	14.87	12.52	5.09	2.35	0.78
CRUXEval	52.32	18.88	34.76	12.7	0.06	15.58
Instruction Following
IFEval	83.5	70.77	45.33	61.48	55.34	54.26
Alpaca-Eval	27.12	21.89	9.54	17.87	9.38	6.98
MTBench	8.53	7.61	7.1	7.03	6.37	6.03
LiveBench	36.83	40.73	21.65	18.79	14.97	14.1

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

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 family of models were helpful to your work, feel free to give us a cite.

@misc{tiifalconh1,
    title = {Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance},
    url = {https://falcon-lm.github.io/blog/falcon-h1},
    author = {Falcon-LLM Team},
    month = {May},
    year = {2025}
}

tiiuae/Falcon-H1-1.5B-Deep-Instruct-GPTQ-Int4

Table of Contents

TL;DR

Model Details

Model Description

Training details

Usage

Inference

🤗 transformers

vLLM

llama.cpp

Evaluation

Useful links

Citation

tiiuae/
Falcon-H1-1.5B-Deep-Instruct-GPTQ-Int4

`llama.cpp`