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)# 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
Download server/tests/test_service.py from StandardThinking/StandardOne-3B: direct link, hf CLI and curl.
- Browser
- Download file 17.1 kB
-
https://huggingface.co/StandardThinking/StandardOne-3B/resolve/main/server/tests/test_service.py
- Command line
-
hf download hf://StandardThinking/StandardOne-3B/server/tests/test_service.py
-
curl -L -o test_service.py https://huggingface.co/StandardThinking/StandardOne-3B/resolve/main/server/tests/test_service.py
17.1 kB
| import asyncio | |
| import math | |
| import unittest | |
| from fastapi.testclient import TestClient | |
| from jev_adapter.__main__ import positive_temperature, temperature_map | |
| from jev_adapter.backend import AdapterError, ScoringResult | |
| from jev_adapter.protocol import SystemOneRequest, probabilities_from_logprobs | |
| from jev_adapter.server import create_app | |
| from jev_adapter.service import SystemOneService | |
| def payload(): | |
| return { | |
| "model": "test-model", | |
| "state": {"text": "결제가 두 번 되었어요. 환불해 주세요."}, | |
| "questions": { | |
| "route": { | |
| "type": "choice", | |
| "instructions": "담당 부서", | |
| "criteria": {"billing": "청구", "technical": "기술"}, | |
| }, | |
| "refund": {"type": "noul", "instructions": "환불 요청인가?"}, | |
| "urgency": { | |
| "type": "score", | |
| "instructions": "긴급도", | |
| "criteria": ["낮음", "중간", "높음"], | |
| }, | |
| }, | |
| } | |
| class FakeBackend: | |
| model = "test-model" | |
| def __init__(self, delay=0, failure=False): | |
| self.calls = [] | |
| self.active = self.peak = self.cancelled = 0 | |
| self.started = self.closed = False | |
| self.delay, self.failure = delay, failure | |
| async def start(self): | |
| self.started = True | |
| async def close(self): | |
| self.closed = True | |
| def labels(self, count): | |
| return tuple(chr(65 + i) for i in range(count)), tuple(range(65, 65 + count)) | |
| async def evaluate(self, prompt, images, labels, token_ids, assistant_prefix): | |
| self.calls.append((prompt, images, labels, token_ids, assistant_prefix)) | |
| self.active += 1 | |
| self.peak = max(self.peak, self.active) | |
| try: | |
| if self.failure: | |
| raise AdapterError( | |
| "backend_unavailable", "Engine unavailable.", status=502 | |
| ) | |
| await asyncio.sleep(self.delay) | |
| return ScoringResult( | |
| tuple(math.log(0.8 if i == 0 else 0.1) for i in range(len(labels))), 20 | |
| ) | |
| except asyncio.CancelledError: | |
| self.cancelled += 1 | |
| raise | |
| finally: | |
| self.active -= 1 | |
| class TestService(unittest.IsolatedAsyncioTestCase): | |
| async def test_mixed_questions_keep_images_and_build_typed_response(self): | |
| backend = FakeBackend() | |
| body = payload() | |
| body["images"] = ["data:image/png;base64,aGVsbG8="] | |
| result = await SystemOneService(backend).score( | |
| SystemOneRequest.model_validate(body) | |
| ) | |
| self.assertEqual(result["answers"]["route"]["choice"], "billing") | |
| self.assertAlmostEqual(result["answers"]["refund"]["noul"], 8 / 9) | |
| self.assertAlmostEqual(result["answers"]["urgency"]["score"], 0.3) | |
| self.assertEqual(result["usage"], {"input_tokens": 60, "output_tokens": 0}) | |
| self.assertEqual(result["metadata"]["evaluations"], 3) | |
| for call in backend.calls: | |
| self.assertEqual(call[1], body["images"]) | |
| self.assertNotIn("담당 부서", backend.calls[1][0]) | |
| async def test_rotations_preserve_canonical_option_mapping(self): | |
| body = payload() | |
| body["questions"] = {"route": body["questions"]["route"]} | |
| body["options"] = {"permutations": 2, "return_logprobs": True} | |
| result = await SystemOneService(FakeBackend()).score( | |
| SystemOneRequest.model_validate(body) | |
| ) | |
| route = result["answers"]["route"] | |
| self.assertAlmostEqual(route["probabilities"]["billing"], 0.5) | |
| self.assertAlmostEqual(route["confidence"], 0) | |
| self.assertEqual(len(route["logprobs"]), 2) | |
| async def test_concurrency_limit_applies_across_requests(self): | |
| backend = FakeBackend(delay=0.01) | |
| service = SystemOneService(backend, max_concurrency=2) | |
| await asyncio.gather( | |
| *( | |
| service.score(SystemOneRequest.model_validate(payload())) | |
| for _ in range(3) | |
| ) | |
| ) | |
| self.assertEqual(len(backend.calls), 9) | |
| self.assertEqual(backend.peak, 2) | |
| async def test_cancellation_closes_all_pending_work(self): | |
| backend = FakeBackend(delay=10) | |
| task = asyncio.create_task( | |
| SystemOneService(backend, max_concurrency=2).score( | |
| SystemOneRequest.model_validate(payload()) | |
| ) | |
| ) | |
| while backend.active != 2: | |
| await asyncio.sleep(0) | |
| task.cancel() | |
| with self.assertRaises(asyncio.CancelledError): | |
| await task | |
| self.assertEqual(backend.cancelled, 2) | |
| self.assertEqual(backend.active, 0) | |
| self.assertEqual(len(backend.calls), 2) | |
| async def test_wrong_model_rejected_before_inference(self): | |
| backend = FakeBackend() | |
| body = payload() | |
| body["model"] = "wrong-model" | |
| with self.assertRaises(AdapterError) as error: | |
| await SystemOneService(backend).score(SystemOneRequest.model_validate(body)) | |
| self.assertEqual(error.exception.status, 404) | |
| self.assertEqual(backend.calls, []) | |
| class TestDefaultTemperature(unittest.IsolatedAsyncioTestCase): | |
| """A server-side default fills an omitted options.temperature only.""" | |
| vector = tuple(math.log(0.8 if i == 0 else 0.1) for i in range(3)) | |
| async def score(self, body, **service_options): | |
| service = SystemOneService(FakeBackend(), **service_options) | |
| return await service.score(SystemOneRequest.model_validate(body)) | |
| def route_probabilities(self, result): | |
| return list(result["answers"]["urgency"]["probabilities"].values()) | |
| async def test_unset_default_keeps_request_default_of_one(self): | |
| result = await self.score(payload()) | |
| self.assertEqual(result["metadata"]["temperature"], 1.0) | |
| expected = probabilities_from_logprobs(self.vector, 1.0) | |
| for actual, wanted in zip(self.route_probabilities(result), expected): | |
| self.assertAlmostEqual(actual, wanted) | |
| async def test_omitted_temperature_takes_server_default(self): | |
| for body in (payload(), {**payload(), "options": {"permutations": 1}}): | |
| result = await self.score(body, default_temperature=2.4) | |
| self.assertEqual(result["metadata"]["temperature"], 2.4) | |
| expected = probabilities_from_logprobs(self.vector, 2.4) | |
| for actual, wanted in zip(self.route_probabilities(result), expected): | |
| self.assertAlmostEqual(actual, wanted) | |
| self.assertNotAlmostEqual( | |
| self.route_probabilities(result)[0], | |
| probabilities_from_logprobs(self.vector, 1.0)[0], | |
| ) | |
| async def test_explicit_request_temperature_wins_over_default(self): | |
| body = {**payload(), "options": {"temperature": 1.0}} | |
| result = await self.score(body, default_temperature=2.4) | |
| self.assertEqual(result["metadata"]["temperature"], 1.0) | |
| expected = probabilities_from_logprobs(self.vector, 1.0) | |
| for actual, wanted in zip(self.route_probabilities(result), expected): | |
| self.assertAlmostEqual(actual, wanted) | |
| body = {**payload(), "options": {"temperature": 3.0}} | |
| result = await self.score(body, default_temperature=2.4) | |
| self.assertEqual(result["metadata"]["temperature"], 3.0) | |
| async def test_disabled_scaling_ignores_default(self): | |
| body = {**payload(), "options": {"temperature_scaling": False}} | |
| result = await self.score(body, default_temperature=2.4) | |
| self.assertEqual(result["metadata"]["temperature"], 1.0) | |
| expected = probabilities_from_logprobs(self.vector, 1.0) | |
| for actual, wanted in zip(self.route_probabilities(result), expected): | |
| self.assertAlmostEqual(actual, wanted) | |
| async def test_default_does_not_mutate_the_request(self): | |
| request = SystemOneRequest.model_validate(payload()) | |
| service = SystemOneService(FakeBackend(), default_temperature=2.4) | |
| await service.score(request) | |
| self.assertEqual(request.options.temperature, 1.0) | |
| self.assertNotIn("temperature", request.options.model_fields_set) | |
| self.assertEqual(service.effective_options(request).temperature, 2.4) | |
| def test_invalid_default_temperature_rejected_at_construction(self): | |
| for value in (0, -1.0, float("inf"), float("nan"), True, "2.4", None): | |
| with self.assertRaises((ValueError, TypeError)): | |
| SystemOneService(FakeBackend(), default_temperature=value) | |
| self.assertEqual( | |
| SystemOneService(FakeBackend(), default_temperature=2).default_temperature, | |
| 2.0, | |
| ) | |
| async def test_temperature_by_type_applies_per_answer_type(self): | |
| result = await self.score( | |
| payload(), default_temperature=2.4, temperature_by_type={"score": 3.0, "noul": 1.5} | |
| ) | |
| urgency = list(result["answers"]["urgency"]["probabilities"].values()) | |
| for actual, wanted in zip(urgency, probabilities_from_logprobs(self.vector, 3.0)): | |
| self.assertAlmostEqual(actual, wanted) | |
| route = list(result["answers"]["route"]["probabilities"].values()) | |
| for actual, wanted in zip(route, probabilities_from_logprobs(self.vector[:2], 2.4)): | |
| self.assertAlmostEqual(actual, wanted) | |
| self.assertEqual(result["metadata"]["temperature"], 2.4) | |
| self.assertEqual(result["metadata"]["temperature_by_type"], {"score": 3.0, "noul": 1.5}) | |
| async def test_explicit_request_temperature_wins_over_temperature_by_type(self): | |
| body = {**payload(), "options": {"temperature": 1.0}} | |
| result = await self.score(body, temperature_by_type={"score": 3.0}) | |
| urgency = list(result["answers"]["urgency"]["probabilities"].values()) | |
| for actual, wanted in zip(urgency, probabilities_from_logprobs(self.vector, 1.0)): | |
| self.assertAlmostEqual(actual, wanted) | |
| self.assertNotIn("temperature_by_type", result["metadata"]) | |
| async def test_disabled_scaling_ignores_temperature_by_type(self): | |
| body = {**payload(), "options": {"temperature_scaling": False}} | |
| result = await self.score(body, temperature_by_type={"score": 3.0}) | |
| urgency = list(result["answers"]["urgency"]["probabilities"].values()) | |
| for actual, wanted in zip(urgency, probabilities_from_logprobs(self.vector, 1.0)): | |
| self.assertAlmostEqual(actual, wanted) | |
| self.assertNotIn("temperature_by_type", result["metadata"]) | |
| def test_invalid_temperature_by_type_rejected_at_construction(self): | |
| for value in ({"judge": 1.2}, {"score": 0}, {"score": float("nan")}, {"noul": True}): | |
| with self.assertRaises(ValueError): | |
| SystemOneService(FakeBackend(), temperature_by_type=value) | |
| self.assertEqual(SystemOneService(FakeBackend()).temperature_by_type, {}) | |
| def test_cli_temperature_map_type(self): | |
| self.assertEqual(temperature_map("choice=1.6, noul=1.2,score=1.8"), {"choice": 1.6, "noul": 1.2, "score": 1.8}) | |
| self.assertEqual(temperature_map(""), {}) | |
| import argparse | |
| for text in ("choice", "judge=1.2", "choice=0", "choice=1.2,choice=1.3", "noul=abc"): | |
| with self.assertRaises(argparse.ArgumentTypeError): | |
| temperature_map(text) | |
| def test_cli_default_temperature_type(self): | |
| self.assertEqual(positive_temperature("2.4"), 2.4) | |
| self.assertEqual(positive_temperature("1"), 1.0) | |
| import argparse | |
| for text in ("0", "-1", "inf", "nan", "abc", ""): | |
| with self.assertRaises(argparse.ArgumentTypeError): | |
| positive_temperature(text) | |
| class TestPromptWording(unittest.IsolatedAsyncioTestCase): | |
| """--prompt-wording is a server-wide setting (CLI/env, not per-request); | |
| the default keeps today's served text unchanged.""" | |
| async def test_default_served_wording_is_unchanged(self): | |
| backend = FakeBackend() | |
| body = payload() | |
| body["questions"] = {"route": body["questions"]["route"]} | |
| await SystemOneService(backend).score(SystemOneRequest.model_validate(body)) | |
| prompt = backend.calls[0][0] | |
| self.assertTrue(prompt.startswith("Context:\n")) | |
| async def test_native_wording_is_plumbed_into_build_prompt(self): | |
| backend = FakeBackend() | |
| body = payload() | |
| body["questions"] = {"route": body["questions"]["route"]} | |
| service = SystemOneService(backend, prompt_wording="native") | |
| await service.score(SystemOneRequest.model_validate(body)) | |
| prompt = backend.calls[0][0] | |
| self.assertTrue(prompt.startswith("Read the state and question.")) | |
| self.assertIn("\n\nState:\n", prompt) | |
| self.assertNotIn("Context:\n", prompt) | |
| async def test_native_wording_requires_canonical_az_labels(self): | |
| # FakeBackend.labels() already returns canonical A, B, C, ... so this | |
| # documents the happy path; build_prompt itself enforces the | |
| # requirement (see TestNativePromptWording in test_protocol.py) when a | |
| # backend's labels are not canonical. | |
| backend = FakeBackend() | |
| labels, _ = backend.labels(3) | |
| self.assertEqual(labels, ("A", "B", "C")) | |
| def test_invalid_prompt_wording_rejected_at_construction(self): | |
| for value in ("", "SERVED", "native ", None, 1): | |
| with self.assertRaises(ValueError): | |
| SystemOneService(FakeBackend(), prompt_wording=value) | |
| class TestHTTP(unittest.TestCase): | |
| def test_standalone_app_lifecycle_schema_and_alias(self): | |
| backend = FakeBackend() | |
| with TestClient(create_app(backend)) as client: | |
| self.assertTrue(backend.started) | |
| self.assertEqual(client.get("/health").json(), {"status": "ok"}) | |
| self.assertEqual( | |
| client.get("/v1/models").json()["data"][0]["id"], backend.model | |
| ) | |
| body = payload() | |
| body["model"] = "jev-latest" | |
| response = client.post("/v1/systemone", json=body) | |
| self.assertEqual(response.status_code, 200) | |
| self.assertEqual(response.json()["model"], backend.model) | |
| self.assertEqual( | |
| set(response.json()["answers"]), {"route", "refund", "urgency"} | |
| ) | |
| bad = client.post("/v1/systemone", json={"model": backend.model}) | |
| self.assertEqual(bad.status_code, 422) | |
| self.assertEqual(bad.json()["error"]["code"], "invalid_request") | |
| self.assertTrue(backend.closed) | |
| def test_app_default_temperature_applies_when_request_omits_it(self): | |
| with TestClient(create_app(FakeBackend(), default_temperature=2.4)) as client: | |
| response = client.post("/v1/systemone", json=payload()) | |
| self.assertEqual(response.status_code, 200) | |
| self.assertEqual(response.json()["metadata"]["temperature"], 2.4) | |
| body = {**payload(), "options": {"temperature": 1.5}} | |
| response = client.post("/v1/systemone", json=body) | |
| self.assertEqual(response.json()["metadata"]["temperature"], 1.5) | |
| with self.assertRaises(ValueError): | |
| create_app(FakeBackend(), default_temperature=0) | |
| def test_app_prompt_wording_native_reaches_the_backend_over_http(self): | |
| backend = FakeBackend() | |
| with TestClient(create_app(backend, prompt_wording="native")) as client: | |
| body = payload() | |
| body["questions"] = {"route": body["questions"]["route"]} | |
| response = client.post("/v1/systemone", json=body) | |
| self.assertEqual(response.status_code, 200) | |
| self.assertTrue(backend.calls[0][0].startswith("Read the state and question.")) | |
| def test_auth_guards_inference_and_model_discovery(self): | |
| backend = FakeBackend() | |
| with TestClient(create_app(backend, api_key="test-secret")) as client: | |
| self.assertEqual(client.get("/v1/models").status_code, 401) | |
| self.assertEqual( | |
| client.post("/v1/systemone", json=payload()).status_code, 401 | |
| ) | |
| self.assertEqual( | |
| client.post( | |
| "/v1/systemone", | |
| json=payload(), | |
| headers={b"Authorization": b"Bearer caf\xe9"}, | |
| ).status_code, | |
| 401, | |
| ) | |
| self.assertEqual(backend.calls, []) | |
| response = client.post( | |
| "/v1/systemone", | |
| json=payload(), | |
| headers={"Authorization": "Bearer test-secret"}, | |
| ) | |
| self.assertEqual(response.status_code, 200) | |
| def test_upstream_failure_is_not_returned_as_probabilities(self): | |
| with TestClient(create_app(FakeBackend(failure=True))) as client: | |
| response = client.post("/v1/systemone", json=payload()) | |
| self.assertEqual(response.status_code, 502) | |
| self.assertEqual(response.json()["error"]["code"], "backend_unavailable") | |
| self.assertNotIn("answers", response.json()) | |