Wither016/Falcon-H1-1.5B-Instruct

<img src="https://huggingface.co/datasets/tiiuae/documentation-images/resolve/main/falcon_mamba/falcon-h1-logo.png" alt="drawing" width="800"/>

Falcon-H1 is a new series of large language models (LLMs) featuring hybrid architecture designs optimized for both high performance and efficiency across diverse use cases. Unlike earlier Falcon models built solely on Transformer or Mamba architectures, Falcon-H1 adopts a parallel hybrid approach that combines Transformer-based attention with State Space Models (SSMs), known for superior long-context memory and computational efficiency. These models excel across reasoning, mathematics, multilingual tasks, instruction following, and scientific knowledge.

• —Paper: Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance
• —GitHub Repository: https://github.com/tiiuae/Falcon-H1
• —Project Documentation: https://tiiuae.github.io/Falcon-H1/

Table of Contents

1. 1.TL;DR
2. 2.Model Details
3. 3.Training Details
4. 4.Usage
5. 5.Evaluation
6. 6.Citation

TL;DR

Falcon-H1 is the latest evolution in the Falcon family of large language models, built upon an advanced hybrid architecture that integrates both State Space Models (SSMs) and Attention Mechanisms in each block. These models span from 500 million to 34 billion parameters, offering high performance and efficiency. They are optimized for diverse use cases, trained with support for 18 core languages (scalable to 100+), and achieve state-of-the-art multilingual and reasoning performances in instruction following, maths, coding, and scientific knowledge tasks.

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.

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}
}