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metadata
language:
  - ar
  - cs
  - de
  - en
  - es
  - fr
  - hi
  - it
  - ja
  - ko
  - nl
  - pl
  - pt
  - ro
  - ru
  - sv
  - ur
  - zh
library_name: transformers
license: other
license_name: falcon-llm-license
tags:
  - falcon-h1
inference: true
pipeline_tag: text-generation
drawing

Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance

Links

Table of Contents

  1. TL;DR
  2. Model Details
  3. Training Details
  4. Usage
  5. Evaluation
  6. 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
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-H1-3B Qwen3-4B Qwen2.5-3B Gemma3-4B Llama3.2-3B Falcon3-3B
General
BBH 53.69 51.07 46.55 50.01 41.47 45.02
ARC-C 49.57 37.71 43.77 44.88 44.88 48.21
TruthfulQA 53.19 51.75 58.11 51.68 50.27 50.06
HellaSwag 69.85 55.31 64.21 47.68 63.74 64.24
MMLU 68.3 67.01 65.09 59.53 61.74 56.76
Math
GSM8k 84.76 80.44 57.54 77.41 77.26 74.68
MATH-500 74.2 85.0 64.2 76.4 41.2 54.2
AMC-23 55.63 66.88 39.84 48.12 22.66 29.69
AIME-24 11.88 22.29 6.25 6.67 11.67 3.96
AIME-25 13.33 18.96 3.96 13.33 0.21 2.29
Science
GPQA 33.89 28.02 28.69 29.19 28.94 28.69
GPQA_Diamond 38.72 40.74 35.69 28.62 29.97 29.29
MMLU-Pro 43.69 29.75 32.76 29.71 27.44 29.71
MMLU-stem 69.93 67.46 59.78 52.17 51.92 56.11
Code
HumanEval 76.83 84.15 73.78 67.07 54.27 52.44
HumanEval+ 70.73 76.83 68.29 61.59 50.0 45.73
MBPP 79.63 68.78 72.75 77.78 62.17 61.9
MBPP+ 67.46 59.79 60.85 66.93 50.53 55.29
LiveCodeBench 26.81 39.92 11.74 21.14 2.74 3.13
CRUXEval 56.25 69.63 43.26 52.13 17.75 44.38
Instruction Following
IFEval 85.05 84.01 64.26 77.01 74.0 69.1
Alpaca-Eval 31.09 36.51 17.37 39.64 19.69 14.82
MTBench 8.72 8.45 7.79 8.24 7.96 7.79
LiveBench 36.86 51.34 27.32 36.7 26.37 26.01

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

Useful links

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}
}