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, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

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
```

Fix for KV Cache Bug - ouro-cache-fix Package

by EVillacis - opened Nov 14, 2025

Discussion

EVillacis

Nov 14, 2025

# Solution for KV Cache Bug in Ouro-1.4B

I've created a Python package that fixes the KV cache indexing bug affecting inference speed.

## 📦 Package: ouro-cache-fix
- PyPI: https://pypi.org/project/ouro-cache-fix/
- GitHub: https://github.com/Antizana/ouro-cache-fix

## ✨ What it fixes
- Resolves cache out-of-bounds errors during generation
- Improves inference speed by 1.3-1.7x
- Drop-in replacement for DynamicCache

## 🚀 Usage
```python
from transformers import AutoModelForCausalLM
from ouro_cache_fix import UniversalTransformerCache

model = AutoModelForCausalLM.from_pretrained(
    "ByteDance/Ouro-1.4B",
    trust_remote_code=True,
    cache_implementation=UniversalTransformerCache
)

   Tested with transformers 4.36.0+ and torch 2.0.0+.

   Would appreciate feedback and testing!

ridger

ByteDance org Nov 16, 2025

Thank you for this valuable contribution! We've verified that your fix successfully enables Ouro to run with transformers>=4.56.0. We've now merged your KV cache optimization into our model repository and credited your work in the model card's Acknowledgments section. We really appreciate your effort in improving the model's compatibility!

sdi1100041

Nov 19, 2025

After your last update, I get

AttributeError: property 'key_cache' of 'UniversalTransformerCache' object has no setter

even when running the example code from the model card

EVillacis

Dec 3, 2025

After your last update, I get

AttributeError: property 'key_cache' of 'UniversalTransformerCache' object has no setter

even when running the example code from the model card

ByteDance has already included this fix as native functionality in the model, as mentioned in the previous comment.

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