---
library_name: transformers
tags:
- falcon-h1
license: other
license_name: falcon-llm-license
license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
---

#  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
```

Refer to [the official vLLM documentation for more details on building vLLM from source](https://docs.vllm.ai/en/latest/getting_started/installation/gpu.html#build-wheel-from-source).

### 🤗 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
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-H1-34B | Qwen2.5-72B | Qwen2.5-32B | Gemma3-27B | Llama3.1-70B | Llama4-scout |
| --- | --- | --- | --- | --- | --- | --- |
| **General**  | | | | | |
| BBH | **69.36** | 67.77 | 67.45 | 61.6 | 62.78 | 61.71 |
| MMLU | 83.46 | **85.96** | 83.18 | 78.32 | 78.49 | 77.98 |
| ARC-C | 71.25 | **72.44** | 70.48 | 70.31 | 69.2 | 62.97 |
| HellaSwag | 85.68 | 87.57 | 85.13 | 86.19 | **87.78** | 84.01 |
| Winogrande | 82.72 | 83.74 | 82.32 | 82.4 | **85.32** | 78.93 |
| **Math**  | | | | | |
| GSM8k | 76.5 | 89.76 | **90.14** | 81.35 | 80.52 | 83.24 |
| MATH lvl5 | **40.71** | 38.14 | 36.4 | 25.38 | 18.81 | 27.19 |
| **Science**  | | | | | |
| GPQA | **42.7** | 42.28 | 39.68 | 35.82 | 36.49 | 35.99 |
| MMLU-Pro | 57.18 | **60.22** | 58.05 | 49.64 | 47.07 | 50.16 |
| MMLU-stem | 83.82 | **84.81** | 82.81 | 76.59 | 70.35 | 72.57 |
| **Code**  | | | | | |
| HumanEval | **70.12** | 59.15 | 59.76 | 48.78 | 57.32 | 57.32 |
| HumanEval+ | **64.63** | 51.22 | 51.83 | 40.85 | 50.61 | 48.78 |
| MBPP | 83.33 | **87.04** | 83.07 | 76.19 | 78.84 | 77.78 |
| MBPP+ | 70.37 | **70.63** | 68.78 | 61.64 | 66.67 | 64.29 |

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/fwXpMyGc) 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}
}
```