---
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-H1-0.5B-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
- **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-H1-0.5B | Qwen3-0.6B | Qwen2.5-0.5B | Gemma3-1B | Llama3.2-1B | Falcon3-1B |
| --- | --- | --- | --- | --- | --- | --- |
| **General**  | | | | | |
| BBH | **42.91** | 32.95 | 33.26 | 35.86 | 33.21 | 34.47 |
| ARC-C | 37.8 | 31.06 | 33.28 | 34.13 | 34.64 | **43.09** |
| TruthfulQA | 44.12 | **51.65** | 46.19 | 42.17 | 42.08 | 42.31 |
| HellaSwag | 51.93 | 42.17 | 52.38 | 42.24 | 55.3 | **58.53** |
| MMLU | **53.4** | 42.98 | 46.07 | 40.87 | 45.93 | 46.1 |
| **Math**  | | | | | |
| GSM8k | **68.39** | 42.61 | 38.51 | 42.38 | 44.28 | 44.05 |
| MATH-500 | **58.4** | 46.0 | 27.8 | 45.4 | 13.2 | 19.8 |
| AMC-23 | **33.13** | 27.97 | 12.5 | 19.22 | 7.19 | 6.87 |
| AIME-24 | **3.75** | 2.71 | 0.62 | 0.42 | 1.46 | 0.41 |
| AIME-25 | **4.38** | 1.67 | 0.21 | 1.25 | 0.0 | 0.21 |
| **Science**  | | | | | |
| GPQA | **29.95** | 26.09 | 26.85 | 28.19 | 26.59 | 26.76 |
| GPQA_Diamond | 27.95 | 25.08 | 24.24 | 21.55 | 25.08 | **31.31** |
| MMLU-Pro | **31.03** | 16.95 | 18.73 | 14.46 | 16.2 | 18.49 |
| MMLU-stem | **54.55** | 39.3 | 39.83 | 35.39 | 39.16 | 39.64 |
| **Code**  | | | | | |
| HumanEval | **51.83** | 41.46 | 36.59 | 40.85 | 34.15 | 22.56 |
| HumanEval+ | **45.12** | 37.19 | 32.32 | 37.2 | 29.88 | 20.73 |
| MBPP | 42.59 | 56.08 | 46.83 | **57.67** | 33.6 | 20.63 |
| MBPP+ | 33.07 | 47.08 | 39.68 | **50.0** | 29.37 | 17.2 |
| LiveCodeBench | 7.05 | **9.78** | 2.94 | 5.09 | 2.35 | 0.78 |
| CRUXEval | **25.75** | 23.63 | 14.88 | 12.7 | 0.06 | 15.58 |
| **Instruction Following**  | | | | | |
| IFEval | **72.07** | 62.16 | 32.11 | 61.48 | 55.34 | 54.26 |
| Alpaca-Eval | 10.79 | 9.59 | 3.26 | **17.87** | 9.38 | 6.98 |
| MTBench | **7.06** | 5.75 | 4.71 | 7.03 | 6.37 | 6.03 |
| LiveBench | 20.8 | **27.78** | 14.27 | 18.79 | 14.97 | 14.1 |

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}
}
```