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 / README.md

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	---
	base_model: StandardThinking/StandardOne-3B
	library_name: transformers
	license: apache-2.0
	pipeline_tag: text-generation
	tags:
	- mistral3
	- decision-model
	- typed-decisions
	- jev
	- jevbench
	- fp8
	- compressed-tensors
	- vision-language
	---

	# 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](https://huggingface.co/StandardThinking/StandardOne-3B#changes-in-v22).

	## 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](https://huggingface.co/StandardThinking/StandardOne-3B).