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

ibrahim-khadraoui-TII

Update README.md

4ea1d41 verified about 1 year ago

5.24 kB

library_name: transformers
tags:
  - falcon-h1
license: other
license_name: falcon-llm-license
license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
base_model: tiiuae/Falcon-H1-34B-Instruct
inference: true

TL;DR
Model Details
Training Details
Usage
Evaluation
Citation

TL;DR

Model Details

Model Description

Developed by: 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.

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:

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

Refer to the official vLLM documentation for more details on building vLLM from source.

🤗 transformers

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

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

While we are working on integrating our architecture directly into llama.cpp library, you can install our fork of the library and use it directly: https://github.com/tiiuae/llama.cpp-Falcon-H1 Use the same installing guidelines as 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, detailed benchmarks.

Useful links

View our release blogpost.
Feel free to join our discord server 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.

@misc{tiifalconh1,
    title = {Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance},
    url = {https://falcon-lm.github.io/blog/falcon-h1},
    author = {Falcon-LLM Team},
    month = {May},
    year = {2025}
}

tiiuae
/

Falcon-H1-34B-Instruct-GGUF

Table of Contents

TL;DR

Model Details

Model Description

Training details

Usage

Inference

🤗 transformers

vLLM

🦙 llama.cpp

Evaluation

Useful links

Citation