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

• Developed by: Technology Innovation Institute
• Model type: Causal decoder-only
• Architecture: Hybrid (Transformers + Mamba2) architecture
• Language(s): English, Multilingual
• License: Falcon-LLM License

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

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

Serving

./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.

TTS represents test time scaling results on few of the benchmarks that we evaluated via DeepConf.

Useful links

• View our release blogpost.
• View our technical report.
• Feel free to join our discord server 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}, 
}