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
File size: 17,817 Bytes
09d4173 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 | """CPU tests of request validation, prompt mapping, and decision probabilities."""
import json
import math
import string
import unittest
from pydantic import ValidationError
from jev_adapter.protocol import (
NATIVE_PROMPT_PREFIX,
SystemOneOptions,
SystemOneRequest,
build_prompt,
plan_question,
probabilities_from_logprobs,
reduce_probabilities,
rotation_order,
)
CANONICAL_LETTERS = tuple(string.ascii_uppercase)
def runner_format_prompt(state, instructions, ordered):
"""Independent, verbatim port of run_suites_rotation.py's format_prompt/
option_texts, used as the parity oracle for wording="native" -- kept
separate from jev_adapter.protocol so a bug in the adapter's own port
cannot also hide in the test."""
text = (
state
if isinstance(state, str)
else json.dumps(state, sort_keys=True, ensure_ascii=False, indent=2, allow_nan=False)
)
lines = []
for position, (name, description) in enumerate(ordered):
lines.append(
f"{CANONICAL_LETTERS[position]}. {name}"
+ (f": {description}" if description else "")
)
return (
f"{NATIVE_PROMPT_PREFIX}\n\nState:\n{text}\n\nQuestion:\n{instructions}\n\nOptions:\n"
+ "\n".join(lines)
)
def request_for(question, **overrides):
return SystemOneRequest.model_validate(
{
"model": "test-model",
"state": "Customer needs help.",
"questions": {"routing": question},
**overrides,
}
)
def choice_question(**overrides):
return {
"type": "choice",
"instructions": "Which team?",
"criteria": {"billing": "Payment issue", "technical": None},
**overrides,
}
class TestSystemOneValidation(unittest.TestCase):
def test_preserves_question_and_option_order_and_structured_values(self):
request = request_for(
choice_question(criteria={"z": ["last", {"nested": True}], "a": None}),
state={"ticket": [7, "안녕하세요", None]},
questions={
"second": choice_question(criteria={"z": {"reason": 1}, "a": None}),
"first": {"type": "noul", "instructions": ["Is urgent?"]},
},
)
self.assertEqual(list(request.questions), ["second", "first"])
plan = plan_question(request.questions["second"])
self.assertEqual(plan.option_names, ("z", "a"))
self.assertEqual(plan.descriptions, ({"reason": 1}, None))
self.assertEqual(request.state["ticket"], [7, "안녕하세요", None])
def test_choice_accepts_255_but_rejects_256(self):
criteria = {str(index): None for index in range(255)}
request = request_for(choice_question(criteria=criteria))
self.assertEqual(
len(plan_question(request.questions["routing"]).option_names), 255
)
with self.assertRaises(ValidationError):
request_for(choice_question(criteria={**criteria, "255": None}))
def test_invalid_question_schemas(self):
cases = [
choice_question(type="arbitrary_json"),
choice_question(criteria={"only": None}),
choice_question(criteria={"": None, "b": None}),
choice_question(criteria={"a": 42, "b": None}),
choice_question(instructions=None),
choice_question(instructions=False),
choice_question(unrecognized=True),
{"type": "noul", "instructions": "Yes?", "criteria": {"true": "yes"}},
{
"type": "noul",
"instructions": "Yes?",
"criteria": {"yes": None, "no": None},
},
{"type": "score", "instructions": "Rate", "criteria": ["only"]},
{"type": "score", "instructions": "Rate", "criteria": [None] * 11},
{
"type": "score",
"instructions": "Rate",
"criteria": {"0": "low", "1": "high"},
},
]
for question in cases:
with self.subTest(question=question), self.assertRaises(ValidationError):
request_for(question)
def test_required_fields_and_request_limits(self):
for updates in (
{"model": " "},
{"model": 3},
{"state": 42},
{"state": None},
{"questions": {}},
{"questions": {" ": choice_question()}},
{"questions": {str(i): choice_question() for i in range(257)}},
):
with self.subTest(updates=updates), self.assertRaises(ValidationError):
request_for(choice_question(), **updates)
for field in ("model", "state", "questions"):
payload = request_for(choice_question()).model_dump()
del payload[field]
with self.subTest(missing=field), self.assertRaises(ValidationError):
SystemOneRequest.model_validate(payload)
def test_rejects_nonfinite_structured_json(self):
for number in (float("nan"), float("inf"), float("-inf")):
with self.subTest(number=number), self.assertRaises(ValidationError):
request_for(choice_question(), state={"nested": [number]})
def test_option_bounds_and_types(self):
for options in (
{"temperature": 0},
{"temperature": -1},
{"temperature": float("inf")},
{"temperature": float("nan")},
{"temperature": True},
{"temperature": "1"},
{"permutations": 0},
{"permutations": 17},
{"permutations": 1.5},
{"permutations": True},
{"return_logprobs": "true"},
{"return_logits": True},
{"assistant_prefix": 3},
):
with self.subTest(options=options), self.assertRaises(ValidationError):
request_for(choice_question(), options=options)
def test_image_sources(self):
request = request_for(
choice_question(),
images=["https://example.com/frame.png", "data:image/png;base64,YQ=="],
)
self.assertEqual(len(request.images), 2)
for image in ("file:///tmp/frame.png", "data:image/png;base64,", "https://", 7):
with self.subTest(image=image), self.assertRaises(ValidationError):
request_for(choice_question(), images=[image])
class TestSystemOnePrompts(unittest.TestCase):
def test_multicharacter_token_labels_request_the_complete_label(self):
plan = plan_question(request_for(choice_question()).questions["routing"])
prompt = build_prompt("state", plan, ["AA", "000"])
self.assertIn("the complete label before the colon", prompt)
self.assertTrue(prompt.endswith("AA: billing: Payment issue\n000: technical"))
def test_shared_prefix_and_rotation_preserve_option_meaning(self):
request = request_for(choice_question(), state={"b": 2, "a": "안녕"})
plan = plan_question(request.questions["routing"])
first = build_prompt(request.state, plan, ["A", "B"])
rotated = build_prompt(request.state, plan, ["A", "B"], rotation_order(2, 1))
self.assertTrue(first.startswith('Context:\n{"a": "안녕", "b": 2}\n\n'))
self.assertEqual(first.split("Options:\n")[0], rotated.split("Options:\n")[0])
self.assertTrue(first.endswith("A: billing: Payment issue\nB: technical"))
self.assertTrue(rotated.endswith("A: technical\nB: billing: Payment issue"))
def test_boolean_default_and_explicit_criteria(self):
request = request_for({"type": "noul", "instructions": "Urgent?"})
plan = plan_question(request.questions["routing"])
self.assertTrue(
build_prompt(request.state, plan, ["A", "B"]).endswith("A: yes\nB: no")
)
request = request_for(
{
"type": "noul",
"instructions": "Urgent?",
"criteria": {"false": "Can wait", "true": "Now"},
}
)
plan = plan_question(request.questions["routing"])
self.assertEqual(plan.option_names, ("true", "false"))
self.assertTrue(
build_prompt(request.state, plan, ["A", "B"]).endswith(
"A: yes: Now\nB: no: Can wait"
)
)
def test_invalid_label_and_option_mappings_fail(self):
plan = plan_question(request_for(choice_question()).questions["routing"])
for labels, order in (
(["A"], None),
(["A", "A"], None),
(["A", "B"], [0, 0]),
(["A", "B"], [0, True]),
):
with (
self.subTest(labels=labels, order=order),
self.assertRaises(ValueError),
):
build_prompt("state", plan, labels, order)
class TestNativePromptWording(unittest.TestCase):
"""wording="native" must match the jevbench-hard runner's own contract
(run_suites_rotation.py: PREFIX/format_prompt/option_texts) byte-for-byte;
``runner_format_prompt`` above is an independent port used as the oracle."""
def test_matches_runner_for_choice_across_rotations_with_json_state(self):
request = request_for(
choice_question(
criteria={"billing": "Payment issue", "technical": None, "sales": ""}
),
state={"ticket": [7, "안녕하세요", None], "z": 1},
)
plan = plan_question(request.questions["routing"])
labels = CANONICAL_LETTERS[:3]
raw_ordered = [
("billing", "Payment issue"),
("technical", None),
("sales", ""),
]
for rotation in range(3):
order = rotation_order(3, rotation)
mine = build_prompt(request.state, plan, labels, order, wording="native")
theirs = runner_format_prompt(
request.state,
plan.instructions,
[raw_ordered[i] for i in order],
)
with self.subTest(rotation=rotation):
self.assertEqual(mine, theirs)
self.assertTrue(mine.startswith(NATIVE_PROMPT_PREFIX + "\n\nState:\n"))
self.assertIn("\n\nQuestion:\n", mine)
def test_matches_runner_for_noul_and_score_with_string_state(self):
noul_plan = plan_question(
request_for(
{
"type": "noul",
"instructions": "Urgent?",
"criteria": {"true": "Now", "false": "Can wait"},
}
).questions["routing"]
)
mine = build_prompt(
"plain state text", noul_plan, ["A", "B"], None, wording="native"
)
theirs = runner_format_prompt(
"plain state text", "Urgent?", [("yes", "Now"), ("no", "Can wait")]
)
self.assertEqual(mine, theirs)
score_plan = plan_question(
request_for(
{"type": "score", "instructions": "Rate", "criteria": ["low", "medium", "high"]}
).questions["routing"]
)
mine = build_prompt("s", score_plan, ["A", "B", "C"], None, wording="native")
theirs = runner_format_prompt(
"s", "Rate", [("0", "low"), ("1", "medium"), ("2", "high")]
)
self.assertEqual(mine, theirs)
def test_falsy_description_is_omitted_like_the_runner(self):
# The runner's `f": {description}" if description else ""` treats an
# empty string the same as no description at all (a truthiness check,
# not `is not None`) -- ported deliberately, not "fixed".
plan = plan_question(
request_for(choice_question(criteria={"a": "", "b": None})).questions[
"routing"
]
)
prompt = build_prompt("s", plan, ["A", "B"], None, wording="native")
self.assertTrue(prompt.endswith("A. a\nB. b"))
def test_rejects_noncanonical_or_out_of_order_labels(self):
plan = plan_question(request_for(choice_question()).questions["routing"])
for labels in (["X", "Y"], ["B", "A"], ["A", "B", "C"]):
with self.subTest(labels=labels), self.assertRaises(ValueError):
build_prompt("s", plan, labels, None, wording="native")
def test_rejects_more_than_26_options(self):
criteria = {f"opt{i}": None for i in range(27)}
plan = plan_question(
request_for(choice_question(criteria=criteria)).questions["routing"]
)
with self.assertRaises(ValueError):
build_prompt("s", plan, CANONICAL_LETTERS[:26] + ("AA",), None, wording="native")
def test_unknown_wording_rejected(self):
plan = plan_question(request_for(choice_question()).questions["routing"])
with self.assertRaises(ValueError):
build_prompt("s", plan, ["A", "B"], None, wording="bogus")
def test_served_wording_is_unchanged_default(self):
plan = plan_question(request_for(choice_question()).questions["routing"])
explicit = build_prompt("s", plan, ["A", "B"], None, wording="served")
implicit = build_prompt("s", plan, ["A", "B"], None)
self.assertEqual(explicit, implicit)
self.assertTrue(implicit.startswith("Context:\n"))
class TestSystemOneProbabilityReduction(unittest.TestCase):
def setUp(self):
self.plan = plan_question(request_for(choice_question()).questions["routing"])
def test_softmax_is_shift_invariant_and_extreme_safe(self):
expected = probabilities_from_logprobs([-1, -3], temperature=2)
actual = probabilities_from_logprobs([-1001, -1003], temperature=2)
for left, right in zip(expected, actual):
self.assertAlmostEqual(left, right)
self.assertEqual(probabilities_from_logprobs([-1e308, 1e308], 1e-300), [0, 1])
def test_temperature_scaling_can_be_disabled(self):
vector = [-1, -3]
normal = reduce_probabilities(self.plan, [vector], [[0, 1]], SystemOneOptions())
disabled = reduce_probabilities(
self.plan,
[vector],
[[0, 1]],
SystemOneOptions(temperature=100, temperature_scaling=False),
)
softened = probabilities_from_logprobs(vector, temperature=100)
self.assertEqual(normal, disabled)
self.assertLess(softened[0], normal["probabilities"]["billing"])
def test_rotation_is_undone_before_probability_average(self):
answer = reduce_probabilities(
self.plan,
[[math.log(0.8), math.log(0.2)], [math.log(0.7), math.log(0.3)]],
[[0, 1], [1, 0]],
SystemOneOptions(permutations=2, return_logprobs=True),
)
self.assertAlmostEqual(answer["probabilities"]["billing"], 0.55)
self.assertAlmostEqual(answer["probabilities"]["technical"], 0.45)
self.assertEqual(answer["choice"], "billing")
self.assertEqual(
answer["logprobs"][1],
{"billing": math.log(0.3), "technical": math.log(0.7)},
)
def test_confidence_uniform_and_certain(self):
uniform = reduce_probabilities(
self.plan, [[-5, -5]], [[0, 1]], SystemOneOptions()
)
certain = reduce_probabilities(
self.plan, [[0, -1000]], [[0, 1]], SystemOneOptions()
)
self.assertAlmostEqual(uniform["confidence"], 0)
self.assertEqual(certain["confidence"], 1)
self.assertEqual(uniform["choice"], "billing") # Stable input-order tie break.
def test_score_expectation_structured_legend_and_entropy(self):
criteria = [{"severity": "low", "examples": ["A"]}, ["medium"], None]
plan = plan_question(
request_for(
{
"type": "score",
"instructions": {"task": "rate"},
"criteria": criteria,
}
).questions["routing"]
)
answer = reduce_probabilities(
plan,
[[math.log(0.25), math.log(0.25), math.log(0.5)]],
[[0, 1, 2]],
SystemOneOptions(),
)
self.assertAlmostEqual(answer["score"], 1.25)
self.assertEqual(
answer["legend"], {"0": criteria[0], "1": criteria[1], "2": None}
)
expected_entropy = -(0.5 * math.log(0.25) + 0.5 * math.log(0.5))
self.assertAlmostEqual(answer["confidence"], 1 - expected_entropy / math.log(3))
def test_noul_returns_probability_of_true_after_rotation(self):
plan = plan_question(
request_for({"type": "noul", "instructions": "Yes?"}).questions["routing"]
)
answer = reduce_probabilities(
plan, [[math.log(0.1), math.log(0.9)]], [[1, 0]], SystemOneOptions()
)
self.assertAlmostEqual(answer["noul"], 0.9)
self.assertEqual(set(answer), {"type", "noul"})
def test_missing_nonfinite_or_invalid_engine_output_fails(self):
for values in (
[None, -1],
[float("nan"), -1],
[float("inf"), -1],
[float("-inf"), -1],
["0", -1],
[True, -1],
):
with self.subTest(values=values), self.assertRaises(ValueError):
reduce_probabilities(self.plan, [values], [[0, 1]], SystemOneOptions())
for vectors, orders in (
([], []),
([[-1]], [[0, 1]]),
([[-1, -2]], []),
([[-1, -2]], [[0, 0]]),
):
with (
self.subTest(vectors=vectors, orders=orders),
self.assertRaises(ValueError),
):
reduce_probabilities(self.plan, vectors, orders, SystemOneOptions())
if __name__ == "__main__":
unittest.main()
|