Instructions to use tiiuae/Falcon-H1-34B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tiiuae/Falcon-H1-34B-Instruct-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="tiiuae/Falcon-H1-34B-Instruct-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("tiiuae/Falcon-H1-34B-Instruct-GGUF", dtype="auto")

llama-cpp-python

How to use tiiuae/Falcon-H1-34B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tiiuae/Falcon-H1-34B-Instruct-GGUF",
	filename="BF16/Falcon-H1-34B-Instruct-BF16-00001-of-00002.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-H1-34B-Instruct-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tiiuae/Falcon-H1-34B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tiiuae/Falcon-H1-34B-Instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tiiuae/Falcon-H1-34B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tiiuae/Falcon-H1-34B-Instruct-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-H1-34B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf tiiuae/Falcon-H1-34B-Instruct-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-H1-34B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tiiuae/Falcon-H1-34B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/tiiuae/Falcon-H1-34B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use tiiuae/Falcon-H1-34B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tiiuae/Falcon-H1-34B-Instruct-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-H1-34B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tiiuae/Falcon-H1-34B-Instruct-GGUF:Q4_K_M

SGLang

How to use tiiuae/Falcon-H1-34B-Instruct-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-H1-34B-Instruct-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-H1-34B-Instruct-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-H1-34B-Instruct-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-H1-34B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use tiiuae/Falcon-H1-34B-Instruct-GGUF with Ollama:
```
ollama run hf.co/tiiuae/Falcon-H1-34B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use tiiuae/Falcon-H1-34B-Instruct-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-H1-34B-Instruct-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-H1-34B-Instruct-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-H1-34B-Instruct-GGUF to start chatting

How to use tiiuae/Falcon-H1-34B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tiiuae/Falcon-H1-34B-Instruct-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-H1-34B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use tiiuae/Falcon-H1-34B-Instruct-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tiiuae/Falcon-H1-34B-Instruct-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-H1-34B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use tiiuae/Falcon-H1-34B-Instruct-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tiiuae/Falcon-H1-34B-Instruct-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "tiiuae/Falcon-H1-34B-Instruct-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use tiiuae/Falcon-H1-34B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/tiiuae/Falcon-H1-34B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use tiiuae/Falcon-H1-34B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tiiuae/Falcon-H1-34B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Falcon-H1-34B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Falcon-H1-34B-Instruct-GGUF / README.md

nielsr HF Staff

Improve model card: Add pipeline tag, paper, code, and project links

d4df340 verified 11 months ago

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	---
	base_model: tiiuae/Falcon-H1-34B-Instruct
	language:
	- ar
	- cs
	- de
	- en
	- es
	- fr
	- hi
	- it
	- ja
	- ko
	- nl
	- pl
	- pt
	- ro
	- ru
	- sv
	- ur
	- zh
	library_name: transformers
	license: other
	license_name: falcon-llm-license
	license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
	tags:
	- falcon-h1
	inference: true
	pipeline_tag: text-generation
	---

	<img src="https://huggingface.co/datasets/tiiuae/documentation-images/resolve/main/falcon_mamba/falcon-h1-logo.png" alt="drawing" width="800"/>

	# Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance

	📚 [Paper](https://huggingface.co/papers/2507.22448) \| 💻 [GitHub Repository](https://github.com/tiiuae/Falcon-H1) \| 📄 [Project Documentation](https://tiiuae.github.io/Falcon-H1/) \| 🖥️ [Hugging Face Demo](https://huggingface.co/spaces/tiiuae/Falcon-H1-playground)

	# Table of Contents

	0. [TL;DR](#TL;DR)
	1. [Model Details](#model-details)
	2. [Training Details](#training-details)
	3. [Usage](#usage)
	4. [Evaluation](#evaluation)
	5. [Citation](#citation)

	# TL;DR
	Falcon-H1 is a new series of large language models (LLMs) featuring a novel hybrid architecture that combines Transformer-based attention with State Space Models (SSMs) for superior long-context memory and computational efficiency. Released in multiple configurations (0.5B to 34B parameters), Falcon-H1 models demonstrate state-of-the-art performance and exceptional parameter and training efficiency. The flagship Falcon-H1-34B matches or outperforms models up to 70B scale, while smaller models also show strong performance relative to their size. These models excel across reasoning, mathematics, multilingual tasks, instruction following, and scientific knowledge, supporting up to 256K context tokens and 18 languages. All models are released under a permissive open-source license.

	# Model Details

	## Model Description

	- Developed by: [https://www.tii.ae](https://www.tii.ae)
	- Model type: Causal decoder-only
	- Architecture: Hybrid Transformers + Mamba architecture
	- Language(s) (NLP): English, Multilingual
	- License: Falcon-LLM License

	# Training details

	For more details about the training protocol of this model, please refer to the [Falcon-H1 technical blogpost](https://falcon-lm.github.io/blog/falcon-h1/) and [Technical Report](https://arxiv.org/abs/2507.22448).

	# Usage

	Currently to use this model you can either rely on Hugging Face `transformers`, `vLLM` or our custom fork of `llama.cpp` library.

	## Inference

	Make sure to install the latest version of `transformers` or `vllm`, eventually install these packages from source:

	```bash
	pip install git+https://github.com/huggingface/transformers.git
	```

	Refer to [the official vLLM documentation for more details on building vLLM from source](https://docs.vllm.ai/en/latest/getting_started/installation/gpu.html#build-wheel-from-source).

	### 🤗 transformers

	Refer to the snippet below to run H1 models using 🤗 transformers:

	```python
	import torch
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_id = "tiiuae/Falcon-H1-1B-Base"

	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	torch_dtype=torch.bfloat16,
	device_map="auto"
	)

	# Perform text generation
	```

	### vLLM

	For vLLM, simply start a server by executing the command below:

	```
	# pip install vllm
	vllm serve tiiuae/Falcon-H1-1B-Instruct --tensor-parallel-size 2 --data-parallel-size 1
	```

	### 🦙 llama.cpp

	Falcon-H1 is compatible with the newest version of `llama.cpp`

	# Evaluation

	Falcon-H1 series perform very well on a variety of tasks, including reasoning tasks.

	\| Tasks \| Falcon-H1-34B \| Qwen3-32B \| Qwen2.5-72B \| Qwen2.5-32B \| Gemma3-27B \| Llama3.3-70B \| Llama4-scout \|
	\| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \|
	\| General \| \| \| \| \| \| \|
	\| BBH \| 70.68 \| 62.47 \| 72.52 \| 68.72 \| 67.28 \| 69.15 \| 64.9 \|
	\| ARC-C \| 61.01 \| 48.98 \| 46.59 \| 44.54 \| 54.52 \| 63.65 \| 56.14 \|
	\| TruthfulQA \| 65.27 \| 58.58 \| 69.8 \| 70.28 \| 64.26 \| 66.15 \| 62.74 \|
	\| HellaSwag \| 81.94 \| 68.89 \| 68.79 \| 73.95 \| 57.25 \| 70.24 \| 65.03 \|
	\| MMLU \| 84.05 \| 80.89 \| 84.42 \| 82.8 \| 78.01 \| 82.08 \| 80.4 \|
	\| Math \| \| \| \| \| \| \|
	\| GSM8k \| 83.62 \| 88.78 \| 82.26 \| 78.47 \| 90.37 \| 93.71 \| 90.37 \|
	\| MATH-500 \| 83.8 \| 82.0 \| 83.6 \| 82.2 \| 90.0 \| 70.6 \| 83.2 \|
	\| AMC-23 \| 69.38 \| 67.34 \| 67.34 \| 68.75 \| 77.81 \| 39.38 \| 69.06 \|
	\| AIME-24 \| 23.75 \| 27.71 \| 17.29 \| 17.92 \| 27.5 \| 12.92 \| 27.92 \|
	\| AIME-25 \| 16.67 \| 19.79 \| 15.21 \| 11.46 \| 22.71 \| 1.25 \| 8.96 \|
	\| Science \| \| \| \| \| \| \|
	\| GPQA \| 41.53 \| 30.2 \| 37.67 \| 34.31 \| 36.49 \| 31.99 \| 31.8 \|
	\| GPQA_Diamond \| 49.66 \| 49.49 \| 44.95 \| 40.74 \| 47.47 \| 42.09 \| 51.18 \|
	\| MMLU-Pro \| 58.73 \| 54.68 \| 56.35 \| 56.63 \| 47.81 \| 53.29 \| 55.58 \|
	\| MMLU-stem \| 83.57 \| 81.64 \| 82.59 \| 82.37 \| 73.55 \| 74.88 \| 75.2 \|
	\| Code \| \| \| \| \| \| \|
	\| HumanEval \| 87.2 \| 90.85 \| 87.2 \| 90.24 \| 86.59 \| 83.53 \| 85.4 \|
	\| HumanEval+ \| 81.71 \| 85.37 \| 80.49 \| 82.32 \| 78.05 \| 79.87 \| 78.7 \|
	\| MBPP \| 83.86 \| 86.24 \| 89.68 \| 87.83 \| 88.36 \| 88.09 \| 81.5 \|
	\| MBPP+ \| 71.43 \| 71.96 \| 75.4 \| 74.07 \| 74.07 \| 73.81 \| 64.8 \|
	\| LiveCodeBench \| 49.71 \| 45.01 \| 54.6 \| 49.12 \| 39.53 \| 40.31 \| 40.12 \|
	\| CRUXEval \| 73.07 \| 78.45 \| 75.63 \| 73.5 \| 74.82 \| 69.53 \| 68.32 \|
	\| Instruction Following \| \| \| \| \| \| \|
	\| IFEval \| 89.37 \| 86.97 \| 86.35 \| 81.79 \| 83.19 \| 89.94 \| 86.32 \|
	\| Alpaca-Eval \| 48.32 \| 64.21 \| 49.29 \| 39.26 \| 56.16 \| 38.27 \| 36.26 \|
	\| MTBench \| 9.2 \| 9.05 \| 9.16 \| 9.09 \| 8.75 \| 8.98 \| 8.98 \|
	\| LiveBench \| 46.26 \| 63.05 \| 54.03 \| 52.92 \| 55.41 \| 53.11 \| 54.21 \|

	You can check more in detail on our [our release blogpost](https://falcon-lm.github.io/blog/falcon-h1/), detailed benchmarks.

	# Useful links

	- View [our release blogpost](https://falcon-lm.github.io/blog/falcon-h1/).
	- View [our technical report](https://arxiv.org/abs/2507.22448).
	- Feel free to join [our discord server](https://discord.gg/trwMYP9PYm) if you have any questions or to interact with our researchers and developers.

	# Citation

	If the Falcon-H1 family of models were helpful to your work, feel free to give us a cite.

	```
	@article{falconh1,
	title={Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance},
	author={Jingwei Zuo and Maksim Velikanov and Ilyas Chahed and Younes Belkada and Dhia Eddine Rhayem and Guillaume Kunsch and Hakim Hacid and Hamza Yous and Brahim Farhat and Ibrahim Khadraoui and Mugariya Farooq and Giulia Campesan and Ruxandra Cojocaru and Yasser Djilali and Shi Hu and Iheb Chaabane and Puneesh Khanna and Mohamed El Amine Seddik and Ngoc Dung Huynh and Phuc Le Khac and Leen AlQadi and Billel Mokeddem and Mohamed Chami and Abdalgader Abubaker and Mikhail Lubinets and Kacper Piskorski and Slim Frikha},
	journal = {arXiv preprint arXiv:2507.22448},
	year={2025}
	}
	```