Instructions to use tiiuae/Falcon-H1-7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tiiuae/Falcon-H1-7B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="tiiuae/Falcon-H1-7B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tiiuae/Falcon-H1-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("tiiuae/Falcon-H1-7B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tiiuae/Falcon-H1-7B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tiiuae/Falcon-H1-7B-Instruct"
# 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-H1-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tiiuae/Falcon-H1-7B-Instruct

SGLang

How to use tiiuae/Falcon-H1-7B-Instruct 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-H1-7B-Instruct" \
    --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-H1-7B-Instruct",
		"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-H1-7B-Instruct" \
        --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-H1-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tiiuae/Falcon-H1-7B-Instruct with Docker Model Runner:
```
docker model run hf.co/tiiuae/Falcon-H1-7B-Instruct
```

JingweiZuo commited on Jul 31, 2025

Commit

41e72f2

verified ·

1 Parent(s): fb92a97

Update README.md

Browse files

Files changed (1) hide show

README.md +26 -7

README.md CHANGED Viewed

@@ -1,5 +1,24 @@
 ---
 library_name: transformers
 tags:
 - falcon-h1
 license: other
@@ -34,7 +53,7 @@ inference: true
 # 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/).
 # Usage
@@ -127,6 +146,7 @@ You can check more in detail on our [our release blogpost](https://falcon-lm.git
 # Useful links
 - View [our release blogpost](https://falcon-lm.github.io/blog/falcon-h1/).
 - 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
@@ -134,11 +154,10 @@ You can check more in detail on our [our release blogpost](https://falcon-lm.git
 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}
 }
 ```

 ---
 library_name: transformers
+language:
+- ar
+- cs
+- de
+- en
+- es
+- fr
+- hi
+- it
+- ja
+- ko
+- nl
+- pl
+- pt
+- ro
+- ru
+- sv
+- ur
+- zh
 tags:
 - falcon-h1
 license: other
 # 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
 # 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}
 }
 ```