Falcon H1 34B Instruct

🚀 Falcon-H1 Transformers Library

The Falcon-H1 library in the transformers ecosystem offers high - performance language models, suitable for a wide range of NLP tasks.

🚀 Quick Start

Currently, to use this model, you can rely on Hugging Face transformers, vLLM, or our custom fork of the llama.cpp library.

Installation

Make sure to install the latest version of transformers or vLLM. You can install these packages from source:

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

Refer to the official vLLM documentation for more details on building vLLM from source.

Inference

🤗 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

While we are working on integrating our architecture directly into the 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.

✨ Features

• Model Type: Causal decoder - only
• Architecture: Hybrid Transformers + Mamba architecture
• Language(s) (NLP): English, Multilingual
• License: Falcon - LLM License

📦 Installation

The installation steps are detailed in the "Quick Start" section. You can choose to install the necessary libraries from source or follow the official guidelines.

💻 Usage Examples

Basic Usage

The basic usage examples are provided in the "Quick Start" section, including using transformers, vLLM, and llama.cpp for inference.

📚 Documentation

Model Details

• 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.

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.
• Feel free to join our discord server if you have any questions or to interact with our researchers and developers.

🔧 Technical Details

The technical details, such as the training protocol, are provided in the Falcon - H1 technical blogpost.

📄 License

The model is under the Falcon - LLM License. You can find the license details at [https://falconllm.tii.ae/falcon - terms - and - conditions.html](https://falconllm.tii.ae/falcon - terms - and - conditions.html)

📄 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}
}