324 lines
9.4 KiB
Markdown
324 lines
9.4 KiB
Markdown
---
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base_model:
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- tiiuae/Falcon-H1-7B-Base
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language:
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- en
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library_name: transformers
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pipeline_tag: text-generation
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license: other
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license_name: falcon-llm-license
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license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
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tags:
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- falcon-h1r
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---
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<img src="https://huggingface.co/datasets/tiiuae/reasoning-images/resolve/main/falcon-h1r-logo.png" alt="drawing" width="800"/>
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# Falcon-H1R-7B
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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).
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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.
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## Model Description
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- **Developed by:** [Technology Innovation Institute](https://www.tii.ae)
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- **Model type:** Causal decoder-only
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- **Architecture:** Hybrid (Transformers + Mamba2) architecture
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- **Language(s):** English, Multilingual
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- **License:** [Falcon-LLM License](https://falconllm.tii.ae/falcon-terms-and-conditions.html)
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## Training details
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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).
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# Usage
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## Setup
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```bash
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git clone https://github.com/ggerganov/llama.cpp
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cd llama.cpp
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cmake -B build
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cmake --build build --config Release -t llama-server
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```
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## Serving
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```bash
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./llama-server -m Falcon-H1R-7B-Q8_0.gguf \
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--temp 0.6 \
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--top-p 0.95 \
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-n 65536 \
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--jinja
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```
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We recommend using a **temperature** of **0.6** and **top-p** as **0.95** with max new tokens up to 65536.
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For supported frameworks, you can adjust the repetition_penalty and presence_penalty parameters to reduce endless repetitions.
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# Evaluation
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Falcon-H1R achieves state of art results in reasoning benchmarks.
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<table border="1" style="width: 100%; text-align: center; border-collapse: collapse;">
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<colgroup>
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<col style="width: 6%;">
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<col style="width: 6%;">
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<col style="background-color: rgba(80, 15, 213, 0.5); width: 5%;">
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<col style="width: 5%;">
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<col style="width: 5%;">
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<col style="width: 5%;">
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<col style="width: 5%;">
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<col style="width: 5%;">
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<col style="width: 5%;">
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<col style="width: 5%;">
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</colgroup>
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<thead>
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<tr>
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<th>Category</th>
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<th>Benchmark</th>
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<th>Falcon-H1R-7B</th>
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<th>Qwen3-8B</th>
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<th>DeepSeek-R1-0528-Qwen3-8B</th>
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<th>Phi-4-Reasoning-Plus-14B</th>
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<th>Apriel-1.5-15b-Thinker</th>
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<th>GPT-OSS-20B</th>
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<th>Qwen3-32B</th>
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<th>Nemotron-H-47B-Reasoning</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td rowspan="5">MATH</td>
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<td>AIME24</td>
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<td><b>88.1</b></td>
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<td>77.9</td>
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<td>83.3</td>
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<td>77.2</td>
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<td>86.2</td>
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<td>83.3</td>
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<td>79.4</td>
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<td>64.6</td>
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</tr>
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<tr>
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<td>AIME25</td>
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<td>83.1</td>
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<td>65.8</td>
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<td>75.8</td>
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<td>71.2</td>
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<td>80.0</td>
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<td><b>84.4</b></td>
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<td>71.0</td>
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<td>51.4</td>
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</tr>
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<tr>
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<td>HMMT25</td>
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<td><b>64.9</b></td>
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<td>41.0</td>
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<td>54.3</td>
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<td>47.7</td>
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<td>61.0</td>
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<td>64.8</td>
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<td>49.8</td>
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<td>34.2</td>
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</tr>
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<tr>
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<td>AMO-BENCH</td>
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<td><b>36.3</b></td>
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<td>14.1</td>
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<td>23.3</td>
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<td>15.0</td>
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<td>22.2</td>
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<td>26.0</td>
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<td>21.3</td>
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<td>7.0</td>
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</tr>
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<tr>
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<td>MATH500</td>
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<td><b>97.4</b></td>
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<td>97.4</td>
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<td>96.8</td>
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<td>95.4</td>
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<td>97.2</td>
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<td>94.8</td>
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<td>96.8</td>
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<td>91.4</td>
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</tr>
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<tr>
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<td rowspan="2">Code</td>
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<td>LCBv5-v6</td>
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<td>68.6</td>
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<td>53.0</td>
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<td>57.2</td>
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<td>53.1</td>
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<td>53.0</td>
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<td><b>72.0</b></td>
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<td>61.0</td>
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<td>47.4</td>
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</tr>
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<tr>
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<td>SciCode (sub/main)</td>
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<td>28.3 / 3.9</td>
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<td>28.3 / 6.7</td>
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<td>22.2 / 2.6</td>
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<td>29.8 / 7.2</td>
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<td>31.9 / 8.2</td>
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<td>34.9 / 6.2</td>
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<td>36.4 / 9.2</td>
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<td>26.1 / 4.6</td>
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</tr>
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<tr>
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<td rowspan="4">General</td>
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<td>GPQA-D</td>
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<td>61.3</td>
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<td>61.2</td>
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<td>61.4</td>
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<td><b>67.9</b></td>
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<td>68.2</td>
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<td>61.2</td>
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<td>67.3</td>
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<td>56.8</td>
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</tr>
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<tr>
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<td>MMLU-Pro</td>
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<td>72.1</td>
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<td>63.5</td>
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<td>69.1</td>
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<td><b>79.2</b></td>
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<td>76.5</td>
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<td>75.6</td>
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<td>73.9</td>
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<td>78.6</td>
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</tr>
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<tr>
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<td>HLE</td>
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<td>11.1</td>
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<td>4.2</td>
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<td>5.6</td>
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<td>5.9</td>
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<td>12.0</td>
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<td>9.8</td>
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<td>8.3</td>
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<td>4.4</td>
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</tr>
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<tr>
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<td>IFBench</td>
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<td>53.4</td>
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<td>35.3</td>
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<td>29.2</td>
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<td>51.7</td>
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<td>55.8</td>
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<td>69.4</td>
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<td>35.4</td>
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<td>34.3</td>
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</tr>
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<tr>
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<td rowspan="2">Agentic Workflows</td>
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<td>𝜏²-Bench Telecom</td>
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<td>25.4</td>
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<td>27.8</td>
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<td></td>
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<td></td>
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<td>68.4</td>
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<td>60.2</td>
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<td>29.8</td>
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<td>11.4</td>
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</tr>
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<tr>
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<td>Terminal-Bench Hard</td>
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<td>4.9</td>
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<td>2.1</td>
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<td>1.4</td>
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<td>2.1</td>
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<td>9.9</td>
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<td>9.9</td>
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<td>2.8</td>
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<td>1.4</td>
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</tr>
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</tbody>
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</table>
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TTS represents test time scaling results on few of the benchmarks that we evaluated via [DeepConf](https://github.com/facebookresearch/deepconf).
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<table border="1" style="width: 100%; text-align: center; border-collapse: collapse;">
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<colgroup>
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<col style="width: 6%;">
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<col style="background-color: rgba(80, 15, 213, 0.5); width: 5%;">
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<col style="width: 5%;">
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<col style="width: 5%;">
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<col style="width: 5%;">
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<col style="width: 5%;">
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<col style="width: 5%;">
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</colgroup>
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<thead>
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<tr>
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<th>Benchmark</th>
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<th>Falcon-H1R-7B</th>
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<th>Qwen3-8B</th>
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<th>DeepSeek-R1-0528-Qwen3-8B</th>
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<th>Nemotron-H-8B</th>
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<th>Phi-4-Reasoning-Plus-14B</th>
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<th>Qwen3-32B</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>AIME24</td>
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<td><b>96.7</b></td>
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<td>80.0</td>
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<td>90.0</td>
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<td>53.3</td>
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<td>86.7</td>
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<td>86.7</td>
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</tr>
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<tr>
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<td>AIME25</td>
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<td><b>96.7</b></td>
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<td>80.0</td>
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<td>82.8</td>
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<td>43.3</td>
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<td>83.3</td>
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<td>86.7</td>
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</tr>
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<tr>
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<td>GPQA-D</td>
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<td>70.2</td>
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<td>60.9</td>
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<td>59.9</td>
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<td>61.1</td>
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<td><b>73.2</b></td>
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<td>70.1</td>
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</tr>
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<tr>
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<td>AMO-Bench</td>
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<td><b>35.9</b></td>
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<td>15.4</td>
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<td>25.6</td>
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<td>7.7</td>
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<td>20.5</td>
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<td>28.2</td>
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</tr>
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</tbody>
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</table>
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# Useful links
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- View [our release blogpost](https://falcon-lm.github.io/blog/falcon-h1r-7b).
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- View [our technical report](https://huggingface.co/papers/2601.02346).
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- Feel free to join [our discord server](https://discord.gg/Cbek57PrZE) if you have any questions or to interact with our researchers and developers.
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# Citation
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If the Falcon-H1R family of reasoning models is helpful to your work, feel free to give us a cite.
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```
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@misc{falcon-h1r,
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title={Falcon-H1R: Pushing the Reasoning Frontiers with a Hybrid Model for Efficient Test-Time Scaling},
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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},
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year={2026},
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eprint={2601.02346},
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archivePrefix={arXiv},
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primaryClass={cs.AI},
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url={https://arxiv.org/abs/2601.02346},
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}
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``` |