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

Libraries

How to use LLM-OS-Models/Ouro-1.4B-Thinking-Terminal-SFT with Transformers:

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

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

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("LLM-OS-Models/Ouro-1.4B-Thinking-Terminal-SFT", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LLM-OS-Models/Ouro-1.4B-Thinking-Terminal-SFT with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/LLM-OS-Models/Ouro-1.4B-Thinking-Terminal-SFT

SGLang

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

Docker Model Runner
How to use LLM-OS-Models/Ouro-1.4B-Thinking-Terminal-SFT with Docker Model Runner:
```
docker model run hf.co/LLM-OS-Models/Ouro-1.4B-Thinking-Terminal-SFT
```

gyung commited on 15 days ago

Commit

d4e7f96

verified ·

1 Parent(s): 1c07c49

Update model card with corrected TB2-lite evaluation

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -20,7 +20,7 @@ base_model: ByteDance/Ouro-1.4B-Thinking
 - Base model: `ByteDance/Ouro-1.4B-Thinking`
 - Training setup: `Terminal SFT`
-- Evaluation snapshot: `2026-05-08 16:08:19 UTC`
 - Evaluation result id: `ouro_1p4b_thinking_terminal_sft`
 ## Quickstart

 - Base model: `ByteDance/Ouro-1.4B-Thinking`
 - Training setup: `Terminal SFT`
+- Evaluation snapshot: `2026-05-09 00:57:29 UTC`
 - Evaluation result id: `ouro_1p4b_thinking_terminal_sft`
 ## Quickstart