Instructions to use StandardThinking/StandardOne-3B-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use StandardThinking/StandardOne-3B-FP8 with Transformers:

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

pipe = pipeline("text-generation", model="StandardThinking/StandardOne-3B-FP8")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("StandardThinking/StandardOne-3B-FP8")
model = AutoModelForMultimodalLM.from_pretrained("StandardThinking/StandardOne-3B-FP8", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use StandardThinking/StandardOne-3B-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "StandardThinking/StandardOne-3B-FP8"
# 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-3B-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/StandardThinking/StandardOne-3B-FP8

SGLang

How to use StandardThinking/StandardOne-3B-FP8 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-3B-FP8" \
    --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-3B-FP8",
		"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-3B-FP8" \
        --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-3B-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use StandardThinking/StandardOne-3B-FP8 with Docker Model Runner:
```
docker model run hf.co/StandardThinking/StandardOne-3B-FP8
```

StandardOne-3B-FP8

StandardOne-3B-FP8 is an FP8 (compressed-tensors, float8_e4m3 weights, dynamic per-token activations) quantization of the released StandardOne-3B decision model. The language-model linear projections (q/k/v/o, gate/up/down) are quantized per-channel FP8 E4M3 with dynamic FP8 activations (llm-compressor's data-free FP8_DYNAMIC recipe, no calibration data required); the vision tower, multi-modal projector, embeddings and lm_head are left unquantized in BF16. It was produced from source revision 68dafd17ead9b8cf6f85c4f08f7f2f3a1e7b9e5c of StandardOne-3B on 2026-09-25 using llm-compressor 0.14.0 (torch 2.14.0, transformers 5.17.0, compressed-tensors 0.19.0); results below.

Validation

Served through SGLang 0.5.20 and jev-adapter (native wording, no system prompt, one option order) on the same items as the BF16 release, measured 2026-09-25. Accuracy is argmax and does not depend on temperature.

Suite	BF16 (StandardOne-3B)	FP8 (this repository)
JevBench public easy (48)	100.00 %	100.00 %
JevBench public standard (72)	88.89 %	88.89 %
JevBench public hard (111)	45.95 %	43.24 %
judge proxy (600)	89.00 %	88.50 %
realistic transfer set (600)	90.83 %	90.50 %
stated-distribution probability (1,036)	80.50 %	79.05 %
hard proxy (600)	42.17 %	41.67 %

Temperature refit on this checkpoint's own served probabilities (same held-out calibration data as the BF16 release): T = 1.3 (BF16 release: T = 1.55). Serve with --default-temperature 1.3.

Downloads last month: 24

Safetensors

Model size

4B params

Tensor type

BF16

F8_E4M3

Model tree for StandardThinking/StandardOne-3B-FP8

Base model

mistralai/Ministral-3-3B-Base-2512

Finetuned

mistralai/Ministral-3-3B-Instruct-2512-BF16

Finetuned

StandardThinking/StandardOne-3B

Quantized

(2)

this model