---
base_model:
- tiiuae/Falcon-H1-7B-Base
language:
- en
library_name: transformers
pipeline_tag: text-generation
license: other
license_name: falcon-llm-license
license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
tags:
- falcon-h1r
---

<img src="https://huggingface.co/datasets/tiiuae/reasoning-images/resolve/main/falcon-h1r-logo.png" alt="drawing" width="800"/>

# Falcon-H1R-7B

This repository presents **Falcon-H1R-7B**, a reasoning-specialized model introduced in the paper [Falcon-H1R: Pushing the Reasoning Frontiers with a Hybrid Model for Efficient Test-Time Scaling](https://huggingface.co/papers/2601.02346). 

Built on top of [Falcon-H1-7B-Base](https://huggingface.co/tiiuae/Falcon-H1-7B-Base), it was trained via cold-start supervised fine-tuning with long reasoning traces and further enhanced by scaling RL with GRPO. The model demonstrates outstanding performance across various benchmark evaluations, including mathematics, programming, instruction following, and general logic.

## Model Description

- **Developed by:** [Technology Innovation Institute](https://www.tii.ae)
- **Model type:** Causal decoder-only
- **Architecture:** Hybrid (Transformers + Mamba2) architecture
- **Language(s):** English, Multilingual
- **License:** [Falcon-LLM License](https://falconllm.tii.ae/falcon-terms-and-conditions.html)

## Training details

For more details about the training protocol of this model, please refer to the [Falcon-H1R technical blogpost](https://falcon-lm.github.io/blog/falcon-h1r-7b) and [Technical Report](https://github.com/tiiuae/falcon-h1r/blob/main/tech_report.pdf).

# Usage

## Setup

```bash
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
cmake -B build
cmake --build build --config Release -t llama-server
```

## Serving

```bash
./llama-server -m Falcon-H1R-7B-Q8_0.gguf \
  --temp 0.6 \
  --top-p 0.95 \
  -n 65536 \
  --jinja
```

We recommend using a **temperature** of **0.6** and **top-p** as **0.95** with max new tokens up to 65536.
For supported frameworks, you can adjust the repetition_penalty and presence_penalty parameters to reduce endless repetitions.

# Evaluation

Falcon-H1R achieves state of art results in reasoning benchmarks. 

<table border="1" style="width: 100%; text-align: center; border-collapse: collapse;">
    <colgroup>
        <col style="width: 6%;">
        <col style="width: 6%;">
        <col style="background-color: rgba(80, 15, 213, 0.5); width: 5%;">  
        <col style="width: 5%;">
        <col style="width: 5%;">
        <col style="width: 5%;">
        <col style="width: 5%;">
        <col style="width: 5%;">
        <col style="width: 5%;">
        <col style="width: 5%;">
    </colgroup>
    <thead>
        <tr>
            <th>Category</th>
            <th>Benchmark</th>
            <th>Falcon-H1R-7B</th>  
            <th>Qwen3-8B</th>
            <th>DeepSeek-R1-0528-Qwen3-8B</th>
            <th>Phi-4-Reasoning-Plus-14B</th>
            <th>Apriel-1.5-15b-Thinker</th>
            <th>GPT-OSS-20B</th>
            <th>Qwen3-32B</th>
            <th>Nemotron-H-47B-Reasoning</th>
        </tr>
    </thead>
    <tbody>
        <tr>
            <td rowspan="5">MATH</td>
            <td>AIME24</td>
            <td><b>88.1</b></td>
            <td>77.9</td>
            <td>83.3</td>
            <td>77.2</td>
            <td>86.2</td>
            <td>83.3</td>
            <td>79.4</td>
            <td>64.6</td>
        </tr>
        <tr>
            <td>AIME25</td>
            <td>83.1</td>
            <td>65.8</td>
            <td>75.8</td>
            <td>71.2</td>
            <td>80.0</td>
            <td><b>84.4</b></td>
            <td>71.0</td>
            <td>51.4</td>
        </tr>
        <tr>
            <td>HMMT25</td>
            <td><b>64.9</b></td>
            <td>41.0</td>
            <td>54.3</td>
            <td>47.7</td>
            <td>61.0</td>
            <td>64.8</td>
            <td>49.8</td>
            <td>34.2</td>
        </tr>
        <tr>
            <td>AMO-BENCH</td>
            <td><b>36.3</b></td>
            <td>14.1</td>
            <td>23.3</td>
            <td>15.0</td>
            <td>22.2</td>
            <td>26.0</td>
            <td>21.3</td>
            <td>7.0</td>
        </tr>
        <tr>
            <td>MATH500</td>
            <td><b>97.4</b></td>
            <td>97.4</td>
            <td>96.8</td>
            <td>95.4</td>
            <td>97.2</td>
            <td>94.8</td>
            <td>96.8</td>
            <td>91.4</td>
        </tr>
        <tr>
            <td rowspan="2">Code</td>
            <td>LCBv5-v6</td>
            <td>68.6</td>
            <td>53.0</td>
            <td>57.2</td>
            <td>53.1</td>
            <td>53.0</td>
            <td><b>72.0</b></td>
            <td>61.0</td>
            <td>47.4</td>
        </tr>
        <tr>
            <td>SciCode (sub/main)</td>
            <td>28.3 / 3.9</td>
            <td>28.3 / 6.7</td>
            <td>22.2 / 2.6</td>
            <td>29.8 / 7.2</td>
            <td>31.9 / 8.2</td>
            <td>34.9 / 6.2</td>
            <td>36.4 / 9.2</td>
            <td>26.1 / 4.6</td>
        </tr>
        <tr>
            <td rowspan="4">General</td>
            <td>GPQA-D</td>
            <td>61.3</td>
            <td>61.2</td>
            <td>61.4</td>
            <td><b>67.9</b></td>
            <td>68.2</td>
            <td>61.2</td>
            <td>67.3</td>
            <td>56.8</td>
        </tr>
        <tr>
            <td>MMLU-Pro</td>
            <td>72.1</td>
            <td>63.5</td>
            <td>69.1</td>
            <td><b>79.2</b></td>
            <td>76.5</td>
            <td>75.6</td>
            <td>73.9</td>
            <td>78.6</td>
        </tr>
        <tr>
            <td>HLE</td>
            <td>11.1</td>
            <td>4.2</td>
            <td>5.6</td>
            <td>5.9</td>
            <td>12.0</td>
            <td>9.8</td>
            <td>8.3</td>
            <td>4.4</td>
        </tr>
        <tr>
            <td>IFBench</td>
            <td>53.4</td>
            <td>35.3</td>
            <td>29.2</td>
            <td>51.7</td>
            <td>55.8</td>
            <td>69.4</td>
            <td>35.4</td>
            <td>34.3</td>
        </tr>
        <tr>
            <td rowspan="2">Agentic Workflows</td>
            <td>𝜏²-Bench Telecom</td>
            <td>25.4</td>
            <td>27.8</td>
            <td></td>
            <td></td>
            <td>68.4</td>
            <td>60.2</td>
            <td>29.8</td>
            <td>11.4</td>
        </tr>
        <tr>
            <td>Terminal-Bench Hard</td>
            <td>4.9</td>
            <td>2.1</td>
            <td>1.4</td>
            <td>2.1</td>
            <td>9.9</td>
            <td>9.9</td>
            <td>2.8</td>
            <td>1.4</td>
        </tr>      
    </tbody>
</table>

TTS represents test time scaling results on few of the benchmarks that we evaluated via [DeepConf](https://github.com/facebookresearch/deepconf).

<table border="1" style="width: 100%; text-align: center; border-collapse: collapse;">
    <colgroup>
        <col style="width: 6%;">
        <col style="background-color: rgba(80, 15, 213, 0.5); width: 5%;">
        <col style="width: 5%;">
        <col style="width: 5%;">
        <col style="width: 5%;">
        <col style="width: 5%;">
        <col style="width: 5%;">
    </colgroup>
    <thead>
        <tr>
            <th>Benchmark</th>
            <th>Falcon-H1R-7B</th>
            <th>Qwen3-8B</th>
            <th>DeepSeek-R1-0528-Qwen3-8B</th>
            <th>Nemotron-H-8B</th>
            <th>Phi-4-Reasoning-Plus-14B</th>
            <th>Qwen3-32B</th>
        </tr>
    </thead>
    <tbody>
        <tr>
            <td>AIME24</td>
            <td><b>96.7</b></td>
            <td>80.0</td>
            <td>90.0</td>
            <td>53.3</td>
            <td>86.7</td>
            <td>86.7</td>
        </tr>
        <tr>
            <td>AIME25</td>
            <td><b>96.7</b></td>
            <td>80.0</td>
            <td>82.8</td>
            <td>43.3</td>
            <td>83.3</td>
            <td>86.7</td>
        </tr>
        <tr>
            <td>GPQA-D</td>
            <td>70.2</td>
            <td>60.9</td>
            <td>59.9</td>
            <td>61.1</td>
            <td><b>73.2</b></td>
            <td>70.1</td>
        </tr>
        <tr>
            <td>AMO-Bench</td>
            <td><b>35.9</b></td>
            <td>15.4</td>
            <td>25.6</td>
            <td>7.7</td>
            <td>20.5</td>
            <td>28.2</td>
        </tr>
    </tbody>
</table>

# Useful links

- View [our release blogpost](https://falcon-lm.github.io/blog/falcon-h1r-7b).
- View [our technical report](https://huggingface.co/papers/2601.02346).
- Feel free to join [our discord server](https://discord.gg/Cbek57PrZE) if you have any questions or to interact with our researchers and developers.

# Citation

If the Falcon-H1R family of reasoning models is helpful to your work, feel free to give us a cite.

```
@misc{falcon-h1r,
      title={Falcon-H1R: Pushing the Reasoning Frontiers with a Hybrid Model for Efficient Test-Time Scaling}, 
      author={Falcon LLM Team and Iheb Chaabane and Puneesh Khanna and Suhail Mohmad and Slim Frikha and Shi Hu and Abdalgader Abubaker and Reda Alami and Mikhail Lubinets and Mohamed El Amine Seddik and Hakim Hacid},
      year={2026},
      eprint={2601.02346},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2601.02346}, 
}
```