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 10 days ago

Commit

21ce2ee

verified ·

1 Parent(s): 5629065

Format training dataset list as a table

Browse files

Files changed (1) hide show

README.md +44 -1

README.md CHANGED Viewed

@@ -229,7 +229,50 @@ Training data is synthetic and format-augmented decision data plus decision item
 datasets (listed below); the JevBench public tiers used only for evaluation carry MIT. Full per-cohort breakdown (row counts, what each covers, licence): [`docs/BENCHMARKS.md`](docs/BENCHMARKS.md#training-data-provenance).
 Public datasets used (train splits where the dataset has one; licence as stated by each dataset; labels come from the
-datasets, distractor options are generated by code): SQuAD 2.0 (CC BY-SA 4.0), ARC (CC BY-SA 4.0), BoolQ (CC BY-SA 3.0), CommonsenseQA (MIT), HellaSwag (MIT), Banking77 (CC BY 4.0), Bias in Bios (MIT), Bitext customer support (CDLA-Sharing-1.0), CLINC150 (CC BY 3.0), Amazon Counterfactual (CC BY 4.0), DBpedia-14 (CC BY-SA 3.0), Dolly 15k (CC BY-SA 3.0), GoEmotions (Apache-2.0), MASSIVE (CC BY 4.0), Twitter Financial News Sentiment (MIT), HelpSteer3 (CC BY 4.0), HelpSteer2 (CC BY 4.0), 2WikiMultihopQA (Apache-2.0), HotpotQA (CC BY-SA 4.0), MuSiQue (CC BY 4.0), QASC (CC BY 4.0), DROP (CC BY-SA 4.0), GSM8K (MIT), TempReason (CC BY-SA 3.0), MultiNLI (OANC / CC BY-SA 3.0 / CC BY 3.0), PAWS (Google terms, free for any purpose), PAWS-X (Google terms, free for any purpose), SNLI (CC BY-SA 4.0), WANLI (CC BY 4.0), ContractNLI (CC BY 4.0), CUAD (CC BY 4.0), ShARC (CC BY-SA 3.0), Jailbreak classification (Apache-2.0), Prompt injections (Apache-2.0), Aegis AI Content Safety 2.0 (CC BY 4.0), Jigsaw Toxic Comment Classification (mirror of the Kaggle data) (CC0 (data); comment text CC BY-SA 3.0 (Wikipedia)), Measuring Hate Speech (CC BY 4.0), Image safety classes (MIT). Upstream ids and the cohort each one feeds: [`docs/BENCHMARKS.md`](docs/BENCHMARKS.md#training-data-provenance).
 An exact-text overlap audit against the public JevBench tiers found 0 exact scenario matches and 181 exact instruction matches — rows in two adequacy-rubric cohorts whose entire instruction field, a generic 58-character adequacy question, is byte-identical to one public hard-tier instruction (0.03 % of the 520,754-row training mixture). These rows are kept and disclosed here rather than regenerated, since the overlap is limited to one rubric question's wording and never touches a scenario or an answer.

 datasets (listed below); the JevBench public tiers used only for evaluation carry MIT. Full per-cohort breakdown (row counts, what each covers, licence): [`docs/BENCHMARKS.md`](docs/BENCHMARKS.md#training-data-provenance).
 Public datasets used (train splits where the dataset has one; licence as stated by each dataset; labels come from the
+datasets, distractor options are generated by code):
+| Dataset | Licence |
+|---|---|
+| SQuAD 2.0 | CC BY-SA 4.0 |
+| ARC | CC BY-SA 4.0 |
+| BoolQ | CC BY-SA 3.0 |
+| CommonsenseQA | MIT |
+| HellaSwag | MIT |
+| Banking77 | CC BY 4.0 |
+| Bias in Bios | MIT |
+| Bitext customer support | CDLA-Sharing-1.0 |
+| CLINC150 | CC BY 3.0 |
+| Amazon Counterfactual | CC BY 4.0 |
+| DBpedia-14 | CC BY-SA 3.0 |
+| Dolly 15k | CC BY-SA 3.0 |
+| GoEmotions | Apache-2.0 |
+| MASSIVE | CC BY 4.0 |
+| Twitter Financial News Sentiment | MIT |
+| HelpSteer3 | CC BY 4.0 |
+| HelpSteer2 | CC BY 4.0 |
+| 2WikiMultihopQA | Apache-2.0 |
+| HotpotQA | CC BY-SA 4.0 |
+| MuSiQue | CC BY 4.0 |
+| QASC | CC BY 4.0 |
+| DROP | CC BY-SA 4.0 |
+| GSM8K | MIT |
+| TempReason | CC BY-SA 3.0 |
+| MultiNLI | OANC / CC BY-SA 3.0 / CC BY 3.0 |
+| PAWS | Google terms, free for any purpose |
+| PAWS-X | Google terms, free for any purpose |
+| SNLI | CC BY-SA 4.0 |
+| WANLI | CC BY 4.0 |
+| ContractNLI | CC BY 4.0 |
+| CUAD | CC BY 4.0 |
+| ShARC | CC BY-SA 3.0 |
+| Jailbreak classification | Apache-2.0 |
+| Prompt injections | Apache-2.0 |
+| Aegis AI Content Safety 2.0 | CC BY 4.0 |
+| Jigsaw Toxic Comment Classification (mirror of the Kaggle data) | CC0 (data); comment text CC BY-SA 3.0 (Wikipedia) |
+| Measuring Hate Speech | CC BY 4.0 |
+| Image safety classes | MIT |
+Upstream ids and the cohort each one feeds: [`docs/BENCHMARKS.md`](docs/BENCHMARKS.md#training-data-provenance).
 An exact-text overlap audit against the public JevBench tiers found 0 exact scenario matches and 181 exact instruction matches — rows in two adequacy-rubric cohorts whose entire instruction field, a generic 58-character adequacy question, is byte-identical to one public hard-tier instruction (0.03 % of the 520,754-row training mixture). These rows are kept and disclosed here rather than regenerated, since the overlap is limited to one rubric question's wording and never touches a scenario or an answer.