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 25

Commit

bfdb6cf

verified ·

1 Parent(s): e884237

Add links to quantized versions

Browse files

Files changed (2) hide show

README.md +9 -0
SHA256SUMS +1 -1

README.md CHANGED Viewed

@@ -64,6 +64,15 @@ served versus offline conditions; measured 24–25 September 2026.*
   decision benchmark is reported.
 - Apache-2.0: base model, adapter, merged weights, and shared server code hosted in the 8B repository.
 ## Quick start
 Follow the setup in [QUICKSTART.md](QUICKSTART.md#standard-one-8b) first: on a CUDA-capable Linux

   decision benchmark is reported.
 - Apache-2.0: base model, adapter, merged weights, and shared server code hosted in the 8B repository.
+## Quantized versions
+| Format | Repository |
+|---|---|
+| FP8 (compressed-tensors, validated with SGLang) | [StandardOne-8B-FP8](https://huggingface.co/StandardThinking/StandardOne-8B-FP8) |
+| GGUF for llama.cpp (BF16, Q8_0 down to IQ2_M, vision projector) | [StandardOne-8B-GGUF](https://huggingface.co/StandardThinking/StandardOne-8B-GGUF) |
+Validation numbers are in each repository's card; the 2-bit GGUF files are experimental.
 ## Quick start
 Follow the setup in [QUICKSTART.md](QUICKSTART.md#standard-one-8b) first: on a CUDA-capable Linux

SHA256SUMS CHANGED Viewed

@@ -2,7 +2,7 @@
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