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

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
```

Improve model card: Add pipeline tag, enrich tags, update license, and add top-level links

by nielsr HF Staff - opened Aug 1, 2025

base: refs/heads/main

←

from: refs/pr/3

Discussion Files changed

+16

-7

nielsr

Aug 1, 2025

This PR enhances the model card for tiiuae/Falcon-H1-1B-Base by:

Adding the pipeline_tag: text-generation to the YAML metadata, which improves discoverability on the Hugging Face Hub (e.g., via https://huggingface.co/models?pipeline_tag=text-generation).
Updating the license to apache-2.0 to use a standard SPDX identifier, aligning with the license indicated in the GitHub repository.
Enriching the tags metadata with multilingual, code-generation, math, reasoning, instruction-tuned, and science to reflect the model's capabilities highlighted in the paper and GitHub README.
Adding a concise summary to the TL;DR section for quick understanding.
Adding explicit links to the paper, GitHub repository, and project homepage at the top of the Markdown content for quick access.
Updating the license information in the "Model Details" section to "Apache 2.0".
Adding the official documentation link to the "Useful links" section.

These changes will improve the model's discoverability, provide more accurate information, and enhance the overall user experience.

Improve model card: Add pipeline tag, enrich tags, update license, and add top-level linksff8d5b18

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