Instructions to use tiiuae/Falcon-H1-Tiny-90M-Instruct-Curriculum-pre-DPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tiiuae/Falcon-H1-Tiny-90M-Instruct-Curriculum-pre-DPO with Transformers:

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

pipe = pipeline("text-generation", model="tiiuae/Falcon-H1-Tiny-90M-Instruct-Curriculum-pre-DPO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tiiuae/Falcon-H1-Tiny-90M-Instruct-Curriculum-pre-DPO")
model = AutoModelForCausalLM.from_pretrained("tiiuae/Falcon-H1-Tiny-90M-Instruct-Curriculum-pre-DPO", device_map="auto")
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-Tiny-90M-Instruct-Curriculum-pre-DPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tiiuae/Falcon-H1-Tiny-90M-Instruct-Curriculum-pre-DPO"
# 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-Tiny-90M-Instruct-Curriculum-pre-DPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tiiuae/Falcon-H1-Tiny-90M-Instruct-Curriculum-pre-DPO

SGLang

How to use tiiuae/Falcon-H1-Tiny-90M-Instruct-Curriculum-pre-DPO 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-Tiny-90M-Instruct-Curriculum-pre-DPO" \
    --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-Tiny-90M-Instruct-Curriculum-pre-DPO",
		"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-Tiny-90M-Instruct-Curriculum-pre-DPO" \
        --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-Tiny-90M-Instruct-Curriculum-pre-DPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tiiuae/Falcon-H1-Tiny-90M-Instruct-Curriculum-pre-DPO with Docker Model Runner:
```
docker model run hf.co/tiiuae/Falcon-H1-Tiny-90M-Instruct-Curriculum-pre-DPO
```

JingweiZuo commited on Jan 15

Commit

008dc03

verified ·

1 Parent(s): 5a49b20

Update README.md

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README.md +5 -5

README.md CHANGED Viewed

@@ -34,7 +34,7 @@ license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
 # Training details
-For more details about the training protocol of this model, please refer to the [Tiny-H1 technical blogpost](https://huggingface.co/spaces/tiiuae/tiny-h1-blogpost).
 # Usage
@@ -87,7 +87,7 @@ ollama run hf.co/tiiuae/Falcon-H1-Tiny-90M-Instruct-Curriculum-pre-DPO:Q8_0
 ### Apple `mlx`
 ```bash
-mlx_lm.chat --model tiiuae/Tiny-H1-SF
 ```
 ### vLLM
@@ -109,7 +109,7 @@ python -m sglang.launch_server \
 # Evaluation
-For detailed evaluation of Tiny-H1 series, please refer to our [technical blogpost](https://huggingface.co/spaces/tiiuae/tiny-h1-blogpost)
 # Useful links
@@ -118,12 +118,12 @@ For detailed evaluation of Tiny-H1 series, please refer to our [technical blogpo
 # Citation
-If the Tiny-H1 family of models were helpful to your work, feel free to give us a cite.
 ```
 @misc{falcon_h1_tiny,
   title={Falcon-H1-Tiny: A series of extremely small, yet powerful language models redefining capabilities at small scale},
   author={Falcon-LLM Team},
-  year={2025},
 }
 ```

 # Training details
+For more details about the training protocol of this model, please refer to the [Falcon-H1-Tiny technical blogpost](https://huggingface.co/spaces/tiiuae/tiny-h1-blogpost).
 # Usage
 ### Apple `mlx`
 ```bash
+mlx_lm.chat --model tiiuae/Falcon-H1-Tiny-90M-Instruct-Curriculum-pre-DPO
 ```
 ### vLLM
 # Evaluation
+For detailed evaluation of Falcon-H1-Tiny series, please refer to our [technical blogpost](https://huggingface.co/spaces/tiiuae/tiny-h1-blogpost)
 # Useful links
 # Citation
+If the Falcon-H1-Tiny family of models were helpful to your work, feel free to give us a cite.
 ```
 @misc{falcon_h1_tiny,
   title={Falcon-H1-Tiny: A series of extremely small, yet powerful language models redefining capabilities at small scale},
   author={Falcon-LLM Team},
+  year={2026},
 }
 ```