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-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use StandardThinking/StandardOne-8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="StandardThinking/StandardOne-8B") 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-8B") model = AutoModelForMultimodalLM.from_pretrained("StandardThinking/StandardOne-8B", 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-8B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "StandardThinking/StandardOne-8B" # 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-8B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/StandardThinking/StandardOne-8B
- SGLang
How to use StandardThinking/StandardOne-8B 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-8B" \ --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-8B", "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-8B" \ --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-8B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use StandardThinking/StandardOne-8B with Docker Model Runner:
docker model run hf.co/StandardThinking/StandardOne-8B
File size: 4,648 Bytes
50ad5a9 7c9e700 50ad5a9 7c9e700 50ad5a9 2b31eeb 50ad5a9 | 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 | """Standalone HTTP process; only the backend client talks to an inference server."""
import asyncio
import logging
import secrets
from contextlib import asynccontextmanager
from fastapi import Depends, FastAPI, Request
from fastapi.exceptions import RequestValidationError
from fastapi.responses import JSONResponse
from .backend import AdapterError, ScoringBackend
from .protocol import SystemOneRequest
from .service import SystemOneService
logger = logging.getLogger(__name__)
def create_app(
backend: ScoringBackend,
*,
api_key: str | None = None,
max_concurrency: int = 32,
alias: str = "jev-latest",
default_temperature: float = 1.0,
temperature_by_type: dict[str, float] | None = None,
prompt_wording: str = "served",
) -> FastAPI:
service = SystemOneService(
backend,
max_concurrency=max_concurrency,
alias=alias,
default_temperature=default_temperature,
temperature_by_type=temperature_by_type,
prompt_wording=prompt_wording,
)
@asynccontextmanager
async def lifespan(app):
try:
await backend.start()
yield
finally:
await backend.close()
app = FastAPI(title="Jev adapter", version="0.1.0", lifespan=lifespan)
async def authorize(request: Request):
if api_key and not secrets.compare_digest(
request.headers.get("authorization", "").encode("utf-8"),
f"Bearer {api_key}".encode(),
):
raise AdapterError(
"unauthorized", "A valid bearer token is required.", status=401
)
@app.exception_handler(AdapterError)
async def adapter_error(request, exc):
return JSONResponse(
status_code=exc.status,
content={
"error": {
"code": exc.code,
"message": str(exc),
"field": exc.field,
}
},
)
@app.exception_handler(RequestValidationError)
async def validation_error(request, exc):
error = exc.errors()[0]
return JSONResponse(
status_code=422,
content={
"error": {
"code": "invalid_request",
"message": error["msg"],
"field": ".".join(str(p) for p in error["loc"] if p != "body"),
}
},
)
@app.get("/health")
async def health():
# Process liveness, not an inference/GPU readiness probe.
return {"status": "ok"}
@app.get("/v1/models", dependencies=[Depends(authorize)])
async def models():
return {
"object": "list",
"data": [
{
"id": backend.model,
"object": "model",
"owned_by": "inference-engine",
**(
{"label_scheme": backend.label_scheme}
if getattr(backend, "label_scheme", None)
else {}
),
},
],
}
@app.post("/v1/systemone", dependencies=[Depends(authorize)])
async def systemone(body: SystemOneRequest, request: Request):
work = asyncio.create_task(service.score(body))
stop_watching = False
async def disconnected():
while not stop_watching:
if await request.is_disconnected():
return
if not stop_watching:
await asyncio.sleep(0.05)
watcher = asyncio.create_task(disconnected())
try:
done, _ = await asyncio.wait(
(work, watcher),
return_when=asyncio.FIRST_COMPLETED,
)
if work in done:
return await work
raise AdapterError(
"client_disconnected", "Client disconnected.", status=499
)
except AdapterError:
raise
except Exception:
logger.exception("Decision scoring failed")
raise AdapterError(
"scoring_failed",
"Could not compute a complete decision.",
status=500,
) from None
finally:
# A disconnect probe's own cancellation scope can absorb cancel().
# Stop explicitly as well, including before another polling sleep.
stop_watching = True
work.cancel()
watcher.cancel()
await asyncio.gather(work, watcher, return_exceptions=True)
return app
|