README.md

---
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-H1-1.5B | Qwen3-1.7B | Qwen2.5-1.5B | Gemma3-1B | Llama3.2-1B | Falcon3-1B |
| --- | --- | --- | --- | --- | --- | --- |
| **General**  | | | | | |
| BBH | **46.57** | 43.05 | 40.55 | 30.26 | 30.72 | 35.24 |
| MMLU | 61.81 | **62.46** | 61.13 | 26.33 | 32.39 | 45.14 |
| ARC-C | 53.24 | **55.72** | 54.27 | 39.33 | 39.42 | 47.87 |
| HellaSwag | 66.76 | 67.09 | **67.86** | 62.94 | 65.73 | 62.3 |
| Winogrande | 65.59 | **66.3** | 64.56 | 62.59 | 62.75 | 61.17 |
| **Math**  | | | | | |
| GSM8k | 52.01 | **70.74** | 63.0 | 2.2 | 7.05 | 34.95 |
| MATH lvl5 | **20.39** | 16.39 | 8.84 | 1.21 | 0.98 | 3.4 |
| **Science**  | | | | | |
| GPQA | 29.11 | **29.45** | 28.36 | 24.66 | 23.57 | 27.85 |
| MMLU-Pro | **35.53** | 33.81 | 28.72 | 11.31 | 11.8 | 16.11 |
| MMLU-stem | **63.37** | 61.53 | 54.93 | 27.59 | 30.19 | 40.06 |
| **Code**  | | | | | |
| HumanEval | 50.0 | **67.68** | 35.37 | 6.71 | 18.9 | 10.37 |
| HumanEval+ | 42.68 | **60.98** | 29.27 | 5.49 | 16.46 | 9.15 |
| MBPP | 65.08 | **67.72** | 60.05 | 12.7 | 35.98 | 12.43 |
| MBPP+ | 55.03 | **58.99** | 49.47 | 9.52 | 29.89 | 9.52 |

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}
}
```