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tiiuae/
Falcon-H1R-7B-GGUF

Name: Falcon-H1R-7B-GGUF
Availability: InStock

tiiuae/Falcon-H1-7B-Base

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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

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.

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

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

Benchmark	Falcon-H1R-7B	Qwen3-8B	DeepSeek-R1-0528-Qwen3-8B	Nemotron-H-8B	Phi-4-Reasoning-Plus-14B	Qwen3-32B
AIME24	96.7	80.0	90.0	53.3	86.7	86.7
AIME25	96.7	80.0	82.8	43.3	83.3	86.7
GPQA-D	70.2	60.9	59.9	61.1	73.2	70.1
AMO-Bench	35.9	15.4	25.6	7.7	20.5	28.2

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}, 
}

tiiuae/Falcon-H1R-7B-GGUF