Instructions to use ByteDance/Ouro-1.4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ByteDance/Ouro-1.4B with Transformers:

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

pipe = pipeline("text-generation", model="ByteDance/Ouro-1.4B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("ByteDance/Ouro-1.4B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ByteDance/Ouro-1.4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ByteDance/Ouro-1.4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ByteDance/Ouro-1.4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ByteDance/Ouro-1.4B

SGLang

How to use ByteDance/Ouro-1.4B 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 "ByteDance/Ouro-1.4B" \
    --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": "ByteDance/Ouro-1.4B",
		"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 "ByteDance/Ouro-1.4B" \
        --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": "ByteDance/Ouro-1.4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ByteDance/Ouro-1.4B with Docker Model Runner:
```
docker model run hf.co/ByteDance/Ouro-1.4B
```

how to finetune the ouro model

by ryota39 - opened Dec 5, 2025

Discussion

ryota39

Dec 5, 2025

thanks for making public the impressive models!! I love this kind of compact but powerfull model.

btw, when I would like to finetune this model, should I write code from scratch? or Is this model already compatible to train the model using libraries like transformers or trl?

Thanks in advance!

ryota39

Dec 5, 2025

•

edited Dec 5, 2025

⚠️ IMPORTANT: Please use transformers<4.56.0 to avoid compatibility issues. We recommend transformers==4.54.1 or earlier versions.

I missed the above caution in README.md. I got it the models are already compatible to transformers.
I also found that ouro is compatible with vllm ouro-vllm
It's enough for me to finetune ouro with these supports from libraries.

Thanks!

ryota39 changed discussion status to closed Dec 5, 2025

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