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)

# pip install -U transformers accelerate
# 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=256)
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

Version: v2.2

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 StandardOne-3B v2.2 (tag v2.2) on 2026-10-04 using llm-compressor 0.14.0 (torch 2.14.0, transformers 5.17.0, compressed-tensors 0.19.0); results below.

Changes in v2.2

Rebuilt from StandardOne-3B v2.2 with the same recipe (recipe.yaml unchanged). The v2.1 to v2.2 score changes are listed in the StandardOne-3B card.

Validation

Both precisions were served the same way through SGLang 0.5.20 and jev-adapter (served wording, one option order, default temperature 1.65), measured 2026-10-04. Accuracy is the most probable answer and does not depend on temperature.

Suite	BF16 (StandardOne-3B v2.2)	FP8 (this repository)	Change (points)
many-option questions, 53–151 options (18,000)	79.97 %	79.57 %	−0.40
the same question set, at most 26 options (750)	85.47 %	84.67 %	−0.80
long-document questions (150)	38.00 %	44.00 %	+6.00
held-out decision set (600)	82.00 %	80.50 %	−1.50
hard proxy (600)	44.67 %	45.17 %	+0.50
realistic transfer set (600)	88.17 %	87.33 %	−0.84
JevBench public easy (48)	100.00 %	97.92 %	−2.08
JevBench public standard (72)	93.06 %	91.67 %	−1.39
JevBench public hard (111)	45.95 %	44.14 %	−1.81

Across 28,406 validation questions with recorded probabilities, FP8 and BF16 gave the same answer for 95.22 %. No separate temperature was fitted for this build; use the serving settings of StandardOne-3B.

Downloads last month: 92

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