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tiiuae/Falcon-H1R-7B-GGUF

sourceHugging Faceotherupdated 9mo agoView on Hugging Face
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<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.

Built on top of 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

Training details

For more details about the training protocol of this model, please refer to the Falcon-H1R technical blogpost and Technical Report.

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 repetitionpenalty and presencepenalty 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.

<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

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}, 
}