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

Libraries

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

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

pipe = pipeline("text-generation", model="StandardThinking/StandardOne-3B")
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")
model = AutoModelForMultimodalLM.from_pretrained("StandardThinking/StandardOne-3B", 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 with vLLM:

Install from pip and serve model

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

Use Docker

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

SGLang

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

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

MyeongHoJeong commited on about 1 month ago

Commit

f71add9

verified ·

1 Parent(s): 815f789

Reformat training source table without changing entries

Browse files

Files changed (1) hide show

docs/BENCHMARKS.md +40 -40

docs/BENCHMARKS.md CHANGED Viewed

@@ -304,46 +304,46 @@ training cohort.
 Public datasets used (train splits where a dataset has one; CUAD: its single release file). Labels come from the datasets;
 distractor options and derived labels are generated by code. Licences as stated by each dataset; attribution to the dataset authors.
-| Dataset | Upstream id | Licence | Cohort |
-|---|---|---|---|
-| SQuAD 2.0 | `rajpurkar/squad_v2` | CC BY-SA 4.0 | `pub-abstain-v1` |
-| ARC | `allenai/ai2_arc` | CC BY-SA 4.0 | `pub-breadth-v1` |
-| BoolQ | `google/boolq` | CC BY-SA 3.0 | `pub-breadth-v1` |
-| CommonsenseQA | `tau/commonsense_qa` | MIT | `pub-breadth-v1` |
-| HellaSwag | `Rowan/hellaswag` | MIT | `pub-breadth-v1` |
-| Banking77 | `PolyAI/banking77` | CC BY 4.0 | `pub-classify-v1` |
-| Bias in Bios | `LabHC/bias_in_bios` | MIT | `pub-classify-v1` |
-| Bitext customer support | `bitext/Bitext-customer-support-llm-chatbot-training-dataset` | CDLA-Sharing-1.0 | `pub-classify-v1` |
-| CLINC150 | `clinc/clinc_oos` | CC BY 3.0 | `pub-classify-v1` |
-| Amazon Counterfactual | `mteb/amazon_counterfactual` | CC BY 4.0 | `pub-classify-v1` |
-| DBpedia-14 | `fancyzhx/dbpedia_14` | CC BY-SA 3.0 | `pub-classify-v1` |
-| Dolly 15k | `databricks/databricks-dolly-15k` | CC BY-SA 3.0 | `pub-classify-v1`, `safety-judge-v1` |
-| GoEmotions | `google-research-datasets/go_emotions` | Apache-2.0 | `pub-classify-v1` |
-| MASSIVE | `AmazonScience/massive` | CC BY 4.0 | `pub-classify-v1` |
-| Twitter Financial News Sentiment | `zeroshot/twitter-financial-news-sentiment` | MIT | `pub-classify-v1` |
-| HelpSteer3 | `nvidia/HelpSteer3` | CC BY 4.0 | `pub-judge-v1` |
-| HelpSteer2 | `nvidia/HelpSteer2` | CC BY 4.0 | `pub-judge-v1` |
-| 2WikiMultihopQA | `framolfese/2WikiMultihopQA` | Apache-2.0 | `pub-multihop-v1` |
-| HotpotQA | `hotpotqa/hotpot_qa` | CC BY-SA 4.0 | `pub-multihop-v1` |
-| MuSiQue | `dgslibisey/MuSiQue` | CC BY 4.0 | `pub-multihop-v1` |
-| QASC | `allenai/qasc` | CC BY 4.0 | `pub-multihop-v1` |
-| DROP | `ucinlp/drop` | CC BY-SA 4.0 | `pub-numeric-v1` |
-| GSM8K | `openai/gsm8k` | MIT | `pub-numeric-v1` |
-| TempReason | `tonytan48/TempReason` | CC BY-SA 3.0 | `pub-numeric-v1` |
-| MultiNLI | `nyu-mll/multi_nli` | OANC / CC BY-SA 3.0 / CC BY 3.0 | `pub-paraphrase-v1` |
-| PAWS | `google-research-datasets/paws` | Google terms, free for any purpose | `pub-paraphrase-v1` |
-| PAWS-X | `google-research-datasets/paws-x` | Google terms, free for any purpose | `pub-paraphrase-v1` |
-| SNLI | `stanfordnlp/snli` | CC BY-SA 4.0 | `pub-paraphrase-v1` |
-| WANLI | `alisawuffles/WANLI` | CC BY 4.0 | `pub-paraphrase-v1` |
-| ContractNLI | `stanfordnlp.github.io/contract-nli` | CC BY 4.0 | `pub-rules-v1` |
-| CUAD | `theatticusproject/cuad` | CC BY 4.0 | `pub-rules-v1` |
-| ShARC | `sharc-data.github.io` | CC BY-SA 3.0 | `pub-rules-v1` |
-| Jailbreak classification | `jackhhao/jailbreak-classification` | Apache-2.0 | `safety-judge-v1` |
-| Prompt injections | `deepset/prompt-injections` | Apache-2.0 | `safety-judge-v1` |
-| Aegis AI Content Safety 2.0 | `nvidia/Aegis-AI-Content-Safety-Dataset-2.0` | CC BY 4.0 | `content-safety-text-v1` |
-| Jigsaw Toxic Comment Classification (mirror of the Kaggle data) | `tasksource/jigsaw_toxicity` | CC0 (data); comment text CC BY-SA 3.0 (Wikipedia) | `content-safety-text-v1` |
-| Measuring Hate Speech | `ucberkeley-dlab/measuring-hate-speech` | CC BY 4.0 | `content-safety-text-v1` |
-| Image safety classes | `deepghs/nsfw_detect` | MIT | `content-safety-image-v1`, `content-safety-image-2d-v1` |
 ## Public-overlap audit and disclosure

 Public datasets used (train splits where a dataset has one; CUAD: its single release file). Labels come from the datasets;
 distractor options and derived labels are generated by code. Licences as stated by each dataset; attribution to the dataset authors.
+| Dataset / upstream id | Licence | Cohort |
+|---|---|---|
+| SQuAD 2.0<br>`rajpurkar/squad_v2` | CC BY-SA 4.0 | `pub-abstain-v1` |
+| ARC<br>`allenai/ai2_arc` | CC BY-SA 4.0 | `pub-breadth-v1` |
+| BoolQ<br>`google/boolq` | CC BY-SA 3.0 | `pub-breadth-v1` |
+| CommonsenseQA<br>`tau/commonsense_qa` | MIT | `pub-breadth-v1` |
+| HellaSwag<br>`Rowan/hellaswag` | MIT | `pub-breadth-v1` |
+| Banking77<br>`PolyAI/banking77` | CC BY 4.0 | `pub-classify-v1` |
+| Bias in Bios<br>`LabHC/bias_in_bios` | MIT | `pub-classify-v1` |
+| Bitext customer support<br>`bitext/Bitext-customer-support-llm-chatbot-training-dataset` | CDLA-Sharing-1.0 | `pub-classify-v1` |
+| CLINC150<br>`clinc/clinc_oos` | CC BY 3.0 | `pub-classify-v1` |
+| Amazon Counterfactual<br>`mteb/amazon_counterfactual` | CC BY 4.0 | `pub-classify-v1` |
+| DBpedia-14<br>`fancyzhx/dbpedia_14` | CC BY-SA 3.0 | `pub-classify-v1` |
+| Dolly 15k<br>`databricks/databricks-dolly-15k` | CC BY-SA 3.0 | `pub-classify-v1`, `safety-judge-v1` |
+| GoEmotions<br>`google-research-datasets/go_emotions` | Apache-2.0 | `pub-classify-v1` |
+| MASSIVE<br>`AmazonScience/massive` | CC BY 4.0 | `pub-classify-v1` |
+| Twitter Financial News Sentiment<br>`zeroshot/twitter-financial-news-sentiment` | MIT | `pub-classify-v1` |
+| HelpSteer3<br>`nvidia/HelpSteer3` | CC BY 4.0 | `pub-judge-v1` |
+| HelpSteer2<br>`nvidia/HelpSteer2` | CC BY 4.0 | `pub-judge-v1` |
+| 2WikiMultihopQA<br>`framolfese/2WikiMultihopQA` | Apache-2.0 | `pub-multihop-v1` |
+| HotpotQA<br>`hotpotqa/hotpot_qa` | CC BY-SA 4.0 | `pub-multihop-v1` |
+| MuSiQue<br>`dgslibisey/MuSiQue` | CC BY 4.0 | `pub-multihop-v1` |
+| QASC<br>`allenai/qasc` | CC BY 4.0 | `pub-multihop-v1` |
+| DROP<br>`ucinlp/drop` | CC BY-SA 4.0 | `pub-numeric-v1` |
+| GSM8K<br>`openai/gsm8k` | MIT | `pub-numeric-v1` |
+| TempReason<br>`tonytan48/TempReason` | CC BY-SA 3.0 | `pub-numeric-v1` |
+| MultiNLI<br>`nyu-mll/multi_nli` | OANC / CC BY-SA 3.0 / CC BY 3.0 | `pub-paraphrase-v1` |
+| PAWS<br>`google-research-datasets/paws` | Google terms, free for any purpose | `pub-paraphrase-v1` |
+| PAWS-X<br>`google-research-datasets/paws-x` | Google terms, free for any purpose | `pub-paraphrase-v1` |
+| SNLI<br>`stanfordnlp/snli` | CC BY-SA 4.0 | `pub-paraphrase-v1` |
+| WANLI<br>`alisawuffles/WANLI` | CC BY 4.0 | `pub-paraphrase-v1` |
+| ContractNLI<br>`stanfordnlp.github.io/contract-nli` | CC BY 4.0 | `pub-rules-v1` |
+| CUAD<br>`theatticusproject/cuad` | CC BY 4.0 | `pub-rules-v1` |
+| ShARC<br>`sharc-data.github.io` | CC BY-SA 3.0 | `pub-rules-v1` |
+| Jailbreak classification<br>`jackhhao/jailbreak-classification` | Apache-2.0 | `safety-judge-v1` |
+| Prompt injections<br>`deepset/prompt-injections` | Apache-2.0 | `safety-judge-v1` |
+| Aegis AI Content Safety 2.0<br>`nvidia/Aegis-AI-Content-Safety-Dataset-2.0` | CC BY 4.0 | `content-safety-text-v1` |
+| Jigsaw Toxic Comment Classification (mirror of the Kaggle data)<br>`tasksource/jigsaw_toxicity` | CC0 (data); comment text CC BY-SA 3.0 (Wikipedia) | `content-safety-text-v1` |
+| Measuring Hate Speech<br>`ucberkeley-dlab/measuring-hate-speech` | CC BY 4.0 | `content-safety-text-v1` |
+| Image safety classes<br>`deepghs/nsfw_detect` | MIT | `content-safety-image-v1`, `content-safety-image-2d-v1` |
 ## Public-overlap audit and disclosure