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
File size: 8,332 Bytes
ea84b46 | 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 | """Download pinned KEV and JevBench evaluation data, without training data."""
from __future__ import annotations
import argparse
import json
from pathlib import Path
import httpx
from jev_adapter.protocol import SystemOneRequest
from .data import (
DEFAULT_SUITES,
KEV_COMMIT,
KEV_RAW,
KEV_REPOSITORY,
SUITES,
parse_partition,
population_counts,
require_sha256,
sha256,
source_provenance,
)
from .jevbench import JEVBENCH_SUITES, prepare_jevbench
def read_source(
path: str, client: httpx.Client | None, source_root: Path | None
) -> bytes:
if source_root is not None:
return (source_root / path).read_bytes()
if client is None:
raise ValueError("an HTTP client or --source-root is required")
response = client.get(f"{KEV_RAW}/{path}")
response.raise_for_status()
return response.content
def prepare_suite(
suite: str,
split: str,
output: Path,
*,
client: httpx.Client | None = None,
source_root: Path | None = None,
allow_test: bool = False,
limit: int | None = None,
) -> dict:
if suite not in SUITES:
raise ValueError(f"unknown suite: {suite}")
if split not in ("development", "test"):
raise ValueError("only development and test are evaluation partitions")
if split == "test" and not allow_test:
raise ValueError(
"locked test requires --allow-test; use development for iteration"
)
if limit is not None and limit < 1:
raise ValueError("--limit must be positive")
spec = SUITES[suite]
manifest_bytes = read_source(f"{spec.path}/manifest.json", client, source_root)
require_sha256(manifest_bytes, spec.manifest_sha256, f"{suite}/manifest.json")
upstream = json.loads(manifest_bytes)
expected_file = upstream["files"][f"{split}.jsonl"]
raw_bytes = read_source(f"{spec.path}/{split}.jsonl", client, source_root)
require_sha256(raw_bytes, expected_file["sha256"], f"{suite}/{split}.jsonl")
records = parse_partition(raw_bytes, suite, split)
counts = population_counts(records)
if counts["records"] != expected_file["records"]:
raise ValueError(f"upstream record count mismatch: {suite}/{split}")
if (
"questions" in expected_file
and counts["questions"] != expected_file["questions"]
):
raise ValueError(f"upstream question count mismatch: {suite}/{split}")
if not records:
raise ValueError(
f"{suite}/{split} is empty; this suite may be development-only"
)
selected = records if limit is None else records[:limit]
for row in selected:
SystemOneRequest.model_validate({"model": "validation-only", **row["record"]})
serialized = (
"".join(
json.dumps(row, ensure_ascii=False, allow_nan=False) + "\n"
for row in selected
)
).encode("utf-8")
directory = output / suite
data_path = directory / f"{split}.jsonl"
manifest_path = directory / f"{split}.manifest.json"
upstream_path = directory / "upstream.manifest.json"
manifest = {
"schema_version": 1,
"suite": suite,
"split": split,
"description": spec.description,
"data_file": data_path.name,
"data_sha256": sha256(serialized),
"upstream": {
"repository": KEV_REPOSITORY,
"commit": KEV_COMMIT,
"path": f"{spec.path}/{split}.jsonl",
"url": f"{KEV_RAW}/{spec.path}/{split}.jsonl",
"sha256": sha256(raw_bytes),
"manifest_sha256": spec.manifest_sha256,
},
"full_partition": counts,
"selected": population_counts(selected),
"selection": {
"method": "full" if len(selected) == len(records) else "prefix",
"requested_limit": limit,
"is_full_partition": len(selected) == len(records),
"note": (
"Full frozen partition."
if len(selected) == len(records)
else "Smoke subset only; not a full-suite result. Pairs may be incomplete."
),
},
"protocol": {
"calibration_applied": False,
"training_data_downloaded": False,
"gold_labels_sent_to_model": False,
"headline_variant": "clean",
"exclude_from_headline_sources": ["unknowable"],
"locked_test": split == "test",
"notes": list(spec.notes),
"upstream_context_policy": upstream.get("context"),
},
"provenance": source_provenance(records, upstream),
}
manifest_payload = (
json.dumps(manifest, ensure_ascii=False, indent=2) + "\n"
).encode()
# Idempotent preparation is safe; refuse to replace any changed frozen artifact.
for path, content in (
(data_path, serialized),
(manifest_path, manifest_payload),
(upstream_path, manifest_bytes),
):
if path.exists() and path.read_bytes() != content:
raise FileExistsError(
f"refusing to overwrite a different prepared artifact: {path}"
)
directory.mkdir(parents=True, exist_ok=True)
data_path.write_bytes(serialized)
manifest_path.write_bytes(manifest_payload)
upstream_path.write_bytes(manifest_bytes)
return manifest
def main(argv: list[str] | None = None) -> None:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--output", type=Path, required=True)
parser.add_argument(
"--suite", action="append", choices=tuple(SUITES) + tuple(JEVBENCH_SUITES)
)
parser.add_argument(
"--split", choices=("development", "test"), default="development"
)
parser.add_argument("--allow-test", action="store_true")
parser.add_argument(
"--limit", type=int, help="deterministic prefix smoke subset per suite"
)
parser.add_argument(
"--source-root", type=Path, help="offline KEV checkout; same SHA256 checks"
)
parser.add_argument(
"--jevbench-source-root", type=Path, help="offline pinned JevBench checkout"
)
args = parser.parse_args(argv)
if args.split == "test" and not args.allow_test:
parser.error("--split test requires --allow-test")
if args.limit is not None and args.limit < 1:
parser.error("--limit must be positive")
suites = args.suite or (
DEFAULT_SUITES if args.split == "test" else (*DEFAULT_SUITES, *JEVBENCH_SUITES)
)
if args.split == "test" and any(suite in JEVBENCH_SUITES for suite in suites):
parser.error("JevBench supplies a public subset, not a locked test split")
with httpx.Client(timeout=60, follow_redirects=True) as client:
for suite in dict.fromkeys(suites):
if suite in JEVBENCH_SUITES:
manifest = prepare_jevbench(
suite,
args.output,
client=client,
source_root=args.jevbench_source_root,
limit=args.limit,
)
print(
json.dumps(
{
"suite": suite,
"split": "public",
"selected": manifest["selected"],
"is_full_partition": manifest["selection"][
"is_full_partition"
],
}
)
)
continue
manifest = prepare_suite(
suite,
args.split,
args.output,
client=client,
source_root=args.source_root,
allow_test=args.allow_test,
limit=args.limit,
)
print(
json.dumps(
{
"suite": suite,
"split": args.split,
"data": str(args.output / suite / f"{args.split}.jsonl"),
"selected": manifest["selected"],
"is_full_partition": manifest["selection"]["is_full_partition"],
}
)
)
if __name__ == "__main__":
main()
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