Instructions to use tiiuae/Falcon-H1R-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tiiuae/Falcon-H1R-7B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="tiiuae/Falcon-H1R-7B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("tiiuae/Falcon-H1R-7B-GGUF", dtype="auto")

llama-cpp-python

How to use tiiuae/Falcon-H1R-7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tiiuae/Falcon-H1R-7B-GGUF",
	filename="Falcon-H1R-7B-BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use tiiuae/Falcon-H1R-7B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tiiuae/Falcon-H1R-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf tiiuae/Falcon-H1R-7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tiiuae/Falcon-H1R-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf tiiuae/Falcon-H1R-7B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf tiiuae/Falcon-H1R-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf tiiuae/Falcon-H1R-7B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf tiiuae/Falcon-H1R-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tiiuae/Falcon-H1R-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/tiiuae/Falcon-H1R-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use tiiuae/Falcon-H1R-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tiiuae/Falcon-H1R-7B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tiiuae/Falcon-H1R-7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tiiuae/Falcon-H1R-7B-GGUF:Q4_K_M

SGLang

How to use tiiuae/Falcon-H1R-7B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "tiiuae/Falcon-H1R-7B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tiiuae/Falcon-H1R-7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "tiiuae/Falcon-H1R-7B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tiiuae/Falcon-H1R-7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use tiiuae/Falcon-H1R-7B-GGUF with Ollama:
```
ollama run hf.co/tiiuae/Falcon-H1R-7B-GGUF:Q4_K_M
```

Unsloth Studio

How to use tiiuae/Falcon-H1R-7B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for tiiuae/Falcon-H1R-7B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for tiiuae/Falcon-H1R-7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tiiuae/Falcon-H1R-7B-GGUF to start chatting

How to use tiiuae/Falcon-H1R-7B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf tiiuae/Falcon-H1R-7B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "tiiuae/Falcon-H1R-7B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use tiiuae/Falcon-H1R-7B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf tiiuae/Falcon-H1R-7B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default tiiuae/Falcon-H1R-7B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use tiiuae/Falcon-H1R-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/tiiuae/Falcon-H1R-7B-GGUF:Q4_K_M
```

Lemonade

How to use tiiuae/Falcon-H1R-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tiiuae/Falcon-H1R-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Falcon-H1R-7B-GGUF-Q4_K_M

List all available models

lemonade list

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.

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
Code	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
Agentic Workflows	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}, 
}