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

Libraries

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

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

pipe = pipeline("text-generation", model="ByteDance/Ouro-1.4B-Thinking", 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-Thinking", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

ridger

wanglne commited on Jun 3

Commit

3aaa222

1 Parent(s): 697afdd

Fix default RoPE init function reference in OuroRotaryEmbedding (#6)

Browse files

- Fix default RoPE init function reference in OuroRotaryEmbedding (934f16998719753da7602b303f9b1d6326fd8060)

Co-authored-by: WangYi <wanglne@users.noreply.huggingface.co>

Files changed (1) hide show

modeling_ouro.py +1 -1

modeling_ouro.py CHANGED Viewed

@@ -479,7 +479,7 @@ class OuroRotaryEmbedding(nn.Module):
         self.config = config
-        rope_init_fn: Callable = compute_default_rope_parameters
         if self.rope_type != "default":
             rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]

         self.config = config
+        rope_init_fn: Callable = self.compute_default_rope_parameters
         if self.rope_type != "default":
             rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]