Instructions to use StandardThinking/StandardOne-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use StandardThinking/StandardOne-8B with Transformers:

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

pipe = pipeline("text-generation", model="StandardThinking/StandardOne-8B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("StandardThinking/StandardOne-8B")
model = AutoModelForMultimodalLM.from_pretrained("StandardThinking/StandardOne-8B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use StandardThinking/StandardOne-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "StandardThinking/StandardOne-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "StandardThinking/StandardOne-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/StandardThinking/StandardOne-8B

SGLang

How to use StandardThinking/StandardOne-8B 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 "StandardThinking/StandardOne-8B" \
    --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": "StandardThinking/StandardOne-8B",
		"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 "StandardThinking/StandardOne-8B" \
        --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": "StandardThinking/StandardOne-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use StandardThinking/StandardOne-8B with Docker Model Runner:
```
docker model run hf.co/StandardThinking/StandardOne-8B
```

MyeongHoJeong commited on Sep 24

Commit

8b40750

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1 Parent(s): 5d1285d

Update README.md

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@@ -52,14 +52,17 @@ served versus offline conditions; measured 24 September 2026.*
 - Send a state and a bounded rubric to receive probabilities for the supplied labels:
   `choice` selects among labeled options, `noul` is yes/no, and `score` uses an ordinal scale.
   The endpoint scores the labels in one forward pass without decoding answer text.
-- In the same-run **offline** comparison with the untuned base, 8B improves on four of six suites,
-  ties on public easy, and declines on public hard. These are not served-endpoint results.
 - Probabilities are temperature-scaled and calibration-checked (hard-tier ECE, distribution
   total-variation) — see Benchmarks below.
-- Multilingual: English plus Japanese, Chinese, Spanish, French, German, Portuguese and Russian, with a
-  smaller Korean share. The retained Pixtral vision tower accepts image data URLs; this card does
-  not report a separate image-input benchmark.
-- Apache-2.0 throughout: base model, adapter, merged weights and server code.
 ## Quick start

 - Send a state and a bounded rubric to receive probabilities for the supplied labels:
   `choice` selects among labeled options, `noul` is yes/no, and `score` uses an ordinal scale.
   The endpoint scores the labels in one forward pass without decoding answer text.
+- In the same-run **offline** comparison with its untuned base, 8B improves on four of six suites,
+  ties on public easy, and declines by 5.41 percentage points on public hard. These are not
+  served-endpoint results.
 - Probabilities are temperature-scaled and calibration-checked (hard-tier ECE, distribution
   total-variation) — see Benchmarks below.
+- The shared training mixture covers English, Japanese, Chinese, Spanish, French, German,
+  Portuguese, Russian, and a smaller Korean share. See the nine-language MASSIVE intent results in
+  [`docs/public-classification-suites.md`](docs/public-classification-suites.md); performance varies
+  by language. The retained Pixtral vision tower accepts image data URLs; no separate image-input
+  decision benchmark is reported.
+- Apache-2.0: base model, adapter, merged weights, and shared server code hosted in the 8B repository.
 ## Quick start