Text Generation
Transformers
Safetensors
mistral3
image-text-to-text
decision-model
typed-decisions
jev
jevbench
calibration
decode-free
multilingual
vision-language
conversational
Instructions to use StandardThinking/StandardOne-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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)# 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=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 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
Download server/tests/test_http_integration.py from StandardThinking/StandardOne-3B: direct link, hf CLI and curl.
- Browser
- Download file 2.03 kB
-
https://huggingface.co/StandardThinking/StandardOne-3B/resolve/main/server/tests/test_http_integration.py
- Command line
-
hf download hf://StandardThinking/StandardOne-3B/server/tests/test_http_integration.py
-
curl -L -o test_http_integration.py https://huggingface.co/StandardThinking/StandardOne-3B/resolve/main/server/tests/test_http_integration.py
2.03 kB
| """Full adapter stack, with only the remote engine's HTTP transport simulated.""" | |
| import httpx | |
| import pytest | |
| from test_service import payload | |
| from test_sglang import Engine | |
| from jev_adapter.server import create_app | |
| from jev_adapter.sglang import SGLangBackend | |
| async def test_public_request_through_real_service_and_sglang_client(): | |
| engine = Engine() | |
| async with httpx.AsyncClient(transport=httpx.MockTransport(engine)) as upstream: | |
| backend = SGLangBackend("http://engine", "qwen", client=upstream) | |
| app = create_app(backend, api_key="client-key") | |
| async with ( | |
| app.router.lifespan_context(app), | |
| httpx.AsyncClient( | |
| transport=httpx.ASGITransport(app), | |
| base_url="http://adapter", | |
| ) as client, | |
| ): | |
| body = payload() | |
| body["model"] = "jev-latest" | |
| body["images"] = ["data:image/png;base64,YQ=="] | |
| body["options"] = {"permutations": 2, "return_logprobs": True} | |
| response = await client.post( | |
| "/v1/systemone", | |
| json=body, | |
| headers={"Authorization": "Bearer client-key"}, | |
| ) | |
| assert response.status_code == 200, response.text | |
| result = response.json() | |
| assert result["model"] == "qwen" | |
| assert set(result["answers"]) == set(body["questions"]) | |
| assert result["answers"]["route"]["probabilities"]["billing"] == pytest.approx( | |
| 0.5 | |
| ) | |
| assert result["usage"] == {"input_tokens": 426, "output_tokens": 0} | |
| assert result["metadata"]["evaluations"] == 6 | |
| generations = [data for path, data, _ in engine.calls if path == "/generate"] | |
| assert len(generations) == 6 | |
| assert len({data["rid"] for data in generations}) == 6 | |
| assert all(data["image_data"] == body["images"] for data in generations) | |
| assert all( | |
| data["sampling_params"]["max_new_tokens"] == 0 for data in generations | |
| ) | |