---
library_name: transformers
language:
- ar
- cs
- de
- en
- es
- fr
- hi
- it
- ja
- ko
- nl
- pl
- pt
- ro
- ru
- sv
- ur
- zh
tags:
- falcon-h1
license: other
license_name: falcon-llm-license
license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
---

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

#  Table of Contents

0. [TL;DR](#TL;DR)
1. [Model Details](#model-details)
2. [Training Details](#training-details)
3. [Usage](#usage)
4. [Evaluation](#evaluation)
5. [Citation](#citation)

# TL;DR

# Model Details

## Model Description

- **Developed by:** [https://www.tii.ae](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](https://falcon-lm.github.io/blog/falcon-h1/).

# 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:

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

For vLLM, make sure to install `vllm>=0.9.0`:

```bash
pip install "vllm>=0.9.0"
```


### 🤗 transformers

Refer to the snippet below to run H1 models using 🤗 transformers:

```python
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. 

| Tasks | Falcon-H1-3B | Qwen3-4B | Qwen2.5-3B | Gemma3-4B | Llama3.2-3B | Falcon3-3B |
| --- | --- | --- | --- | --- | --- | --- |
| **General**  | | | | | |
| BBH | 53.17 | **56.88** | 46.4 | 40.41 | 39.45 | 44.02 |
| MMLU | 68.39 | **72.92** | 65.56 | 59.41 | 55.94 | 56.77 |
| ARC-C | 61.35 | **64.33** | 56.57 | 58.36 | 51.02 | 55.12 |
| HellaSwag | 73.85 | 75.74 | 74.6 | **77.62** | 76.39 | 67.13 |
| Winogrande | 68.11 | 72.3 | 71.03 | **72.77** | 72.22 | 65.11 |
| **Math**  | | | | | |
| GSM8k | 68.31 | **81.65** | 74.6 | 37.6 | 27.82 | 64.67 |
| MATH lvl5 | **25.83** | 24.47 | 16.09 | 6.95 | 1.74 | 11.56 |
| **Science**  | | | | | |
| GPQA | 32.63 | **34.9** | 28.44 | 29.78 | 28.78 | 29.78 |
| MMLU-Pro | 40.58 | **46.18** | 32.12 | 28.34 | 25.08 | 29.03 |
| MMLU-stem | 69.55 | **75.58** | 62.23 | 51.7 | 47.67 | 55.34 |
| **Code**  | | | | | |
| HumanEval | 59.15 | **74.39** | 42.68 | 33.54 | 29.27 | 36.59 |
| HumanEval+ | 53.66 | **68.9** | 35.37 | 28.05 | 26.22 | 31.71 |
| MBPP | 71.43 | **74.6** | 59.52 | 60.05 | 48.94 | 51.85 |
| MBPP+ | 57.94 | **63.76** | 50.53 | 51.32 | 39.42 | 42.06 |

You can check more in detail on our [our release blogpost](https://falcon-lm.github.io/blog/falcon-h1/), detailed benchmarks.

# Useful links

- View [our release blogpost](https://falcon-lm.github.io/blog/falcon-h1/).
- Feel free to join [our discord server](https://discord.gg/trwMYP9PYm) 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}
}
```