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
```

README: fix stale transformers version guidance (currently recommends a broken version window)

#14

by VemVemRu - opened 3 days ago

base: refs/heads/main

←

from: refs/pr/14

Discussion Files changed

+10

-1

Files changed (1) hide show

README.md +10 -1

README.md CHANGED Viewed

@@ -93,7 +93,16 @@ Ouro-1.4B is based on the decoder-only Transformer architecture with parameter s
 ## Quick Start
-**⚠️ IMPORTANT**: Please use `transformers<4.56.0` to avoid compatibility issues. We recommend `transformers==4.54.1` or earlier versions.
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer

 ## Quick Start
+**⚠️ IMPORTANT**: With the current code revision, please use `transformers>=4.56.0` (verified working on `4.57.1`). Earlier versions fail:
+| transformers version | status |
+|---|---|
+| 4.52.x and earlier | ❌ `ImportError: cannot import name 'layer_type_validation'` (`configuration_ouro.py`) |
+| 4.54.x to 4.55.x | ❌ `AttributeError: property 'key_cache' of 'UniversalTransformerCache' object has no setter` (`modeling_ouro.py`) |
+| 4.56.0 and later | ✅ works (verified on `4.57.1`) |
+For `transformers` 5.x support, see PR #13.
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer