---
library_name: transformers
tags:
- falcon-h1
license: other
license_name: falcon-llm-license
license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
base_model: tiiuae/Falcon-H1-7B-Base
inference: true
---

<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-H1-7B | Qwen3-8B | Qwen2.5-7B | Gemma3-12B | Llama3.1-8B | Falcon3-7B | Falcon3-10B |
| --- | --- | --- | --- | --- | --- | --- | --- |
| **General**  | | | | | | |
| BBH | 62.28 | 47.47 | 53.76 | **63.36** | 48.58 | 52.12 | 58.09 |
| ARC-C | **59.98** | 42.06 | 41.38 | 51.96 | 52.39 | 54.35 | 54.44 |
| TruthfulQA | 59.91 | 53.19 | **62.41** | 61.02 | 52.99 | 55.58 | 55.05 |
| HellaSwag | **75.92** | 60.56 | 63.4 | 55.63 | 71.28 | 71.81 | 75.57 |
| MMLU | **76.83** | 71.56 | 73.64 | 72.5 | 68.67 | 70.81 | 74.01 |
| **Math**  | | | | | | |
| GSM8k | 81.65 | 78.92 | 71.95 | **87.49** | 82.49 | 81.05 | 85.06 |
| MATH-500 | 73.4 | 83.8 | 75.8 | **86.2** | 45.8 | 69.0 | 68.6 |
| AMC-23 | 56.72 | **70.78** | 53.91 | 66.88 | 22.81 | 40.0 | 45.78 |
| AIME-24 | 16.04 | **28.33** | 12.29 | 22.5 | 5.42 | 8.75 | 9.79 |
| AIME-25 | 13.96 | **19.17** | 9.58 | 18.75 | 0.42 | 6.25 | 5.42 |
| **Science**  | | | | | | |
| GPQA | **36.33** | 25.84 | 31.79 | 33.98 | 32.72 | 31.21 | 33.39 |
| GPQA_Diamond | **56.9** | 43.1 | 33.0 | 37.71 | 31.31 | 37.21 | 34.68 |
| MMLU-Pro | **51.75** | 34.64 | 43.23 | 39.88 | 36.42 | 40.73 | 44.05 |
| MMLU-stem | **77.61** | 66.89 | 69.36 | 66.54 | 59.31 | 67.43 | 70.57 |
| **Code**  | | | | | | |
| HumanEval | **86.59** | 84.75 | 82.32 | 84.76 | 68.29 | 71.95 | 82.32 |
| HumanEval+ | **81.1** | 79.27 | 73.78 | 75.61 | 61.59 | 65.85 | 75.0 |
| MBPP | 80.69 | 71.96 | 79.63 | **85.71** | 68.25 | 77.25 | 73.28 |
| MBPP+ | 68.78 | 62.7 | 68.25 | **72.22** | 55.03 | 65.87 | 64.02 |
| LiveCodeBench | 35.03 | **45.6** | 32.68 | 30.92 | 15.85 | 12.72 | 19.77 |
| CRUXEval | 66.51 | **72.7** | 56.9 | 67.67 | 21.57 | 55.0 | 59.57 |
| **Instruction Following**  | | | | | | |
| IFEval | **85.35** | 83.43 | 75.25 | 81.51 | 77.04 | 76.59 | 78.84 |
| Alpaca-Eval | 40.23 | **46.13** | 29.48 | 43.55 | 25.48 | 27.56 | 24.31 |
| MTBench | **8.85** | 8.74 | 8.45 | 8.69 | 8.29 | 8.73 | 8.46 |
| LiveBench | 45.74 | **56.19** | 37.13 | 49.23 | 31.73 | 32.35 | 34.3 |

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}
}
```