tiiuae / Falcon-H1-34B-Instruct

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 .

Usage

Currently to use this model you can either rely on Hugging Face transformers , vLLM or our custom fork of 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

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.

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}
}