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: 1,874 Bytes
0f14d00 | 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 | """Smoke-test a running adapter, optionally including actual image bytes."""
import argparse
import base64
import json
import mimetypes
import os
import time
from pathlib import Path
from urllib.request import Request, urlopen
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--base-url", default="http://127.0.0.1:30120")
parser.add_argument(
"--request", type=Path, default=Path(__file__).with_name("request.json")
)
parser.add_argument("--image", type=Path, action="append", default=[])
args = parser.parse_args()
body = json.loads(args.request.read_text())
for path in args.image:
mime = mimetypes.guess_type(path.name)[0]
if not mime or not mime.startswith("image/"):
parser.error(f"Cannot identify image type: {path.name}")
encoded = base64.b64encode(path.read_bytes()).decode("ascii")
body.setdefault("images", []).append(f"data:{mime};base64,{encoded}")
headers = {"Content-Type": "application/json"}
if key := os.environ.get("JEV_API_KEY"):
headers["Authorization"] = f"Bearer {key}"
request = Request(
args.base_url.rstrip("/") + "/v1/systemone",
data=json.dumps(body).encode(),
headers=headers,
)
started = time.perf_counter()
with urlopen(request, timeout=120) as response:
result = json.load(response)
elapsed = (time.perf_counter() - started) * 1000
if result.get("usage", {}).get("output_tokens") != 0:
raise RuntimeError("Expected zero generated tokens")
if set(result.get("answers", {})) != set(body["questions"]):
raise RuntimeError("Missing decision answers")
print(json.dumps(result, indent=2, ensure_ascii=False))
print(f"End-to-end HTTP latency: {elapsed:.1f} ms (not a GPU benchmark)")
if __name__ == "__main__":
main()
|