Instructions to use contextboxai/Can-1.0-31B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use contextboxai/Can-1.0-31B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="contextboxai/Can-1.0-31B")
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("contextboxai/Can-1.0-31B")
model = AutoModelForMultimodalLM.from_pretrained("contextboxai/Can-1.0-31B", 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 contextboxai/Can-1.0-31B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "contextboxai/Can-1.0-31B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "contextboxai/Can-1.0-31B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/contextboxai/Can-1.0-31B

SGLang

How to use contextboxai/Can-1.0-31B 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 "contextboxai/Can-1.0-31B" \
    --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": "contextboxai/Can-1.0-31B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "contextboxai/Can-1.0-31B" \
        --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": "contextboxai/Can-1.0-31B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use contextboxai/Can-1.0-31B with Docker Model Runner:
```
docker model run hf.co/contextboxai/Can-1.0-31B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Cân 1.0 (31B)

Cân (Vietnamese: to weigh; cán cân = the scales of justice) is a decision model. Given a state (any text, JSON or conversation) and one typed question, it returns a calibrated probability distribution over the supplied options in one forward pass. Nothing is generated.

Question types follow TypeSafe /v1/systemone:

choice: pick one label from criteria → choice, probabilities
score: ordered scale (criteria is a list of levels) → expected level score, probabilities
noul: a statement that is true or false → noul = P(true)


Base model	google/gemma-4-31B-it (Apache-2.0)
Fine-tune	LoRA r16 (merged), letter readout at the answer position
Training data	public human-labelled typed decisions (tasksource), procedural rule-application tasks with computed labels, synthetic workflow documents, programmatically generated math/code items with execution-verified labels
Teacher signal	soft labels from perplexity-ai/pplx-decider-v1.1-27b (Apache-2.0) mixed 50/50 with gold labels
Calibration	per (question type, option count) temperature, fitted on held-out non-benchmark data (`calib.json`)
Context	1x H100: states up to 10k tokens read in full, longer ones truncated head+tail (never refused)

Run

Tested configuration: 1x H100 80GB, vLLM 0.28.0.

pip install vllm==0.28.0 transformers
vllm serve contextboxai/Can-1.0-31B --served-model-name jevbeat --port 8890 \
  --max-model-len 12288 --gpu-memory-utilization 0.95 --max-logprobs 20
# decision server (POST :8011/v1/systemone); states over JB_MAX_STATE tokens are truncated head 60% / tail 40%, never refused
JB_TOKENIZER=contextboxai/Can-1.0-31B JB_CALIB=calib.json JB_MAX_STATE=10000 python serve/server.py

Optional (not tested by us): with more GPU memory (e.g. --tensor-parallel-size 2 --max-model-len 98304), raise JB_MAX_STATE to ~90000 to read the longest states in full.

Request: {"state": ..., "questions": {"decision": {"type": ..., "instructions": ..., "criteria": ...}}}. Response: {"model", "answers": {"decision": {...}}, "usage": {"prompt_tokens", "completion_tokens", "total_tokens"}}. More than 10 options -> HTTP 422.

Evaluation (internal proxy, not official)

Proxy slices (ours; not the official benchmark). Capability = (I + C) / 2 after calibration; paired doc-bootstrap where noted.

slice	items	I	C	Capability	Quyet-1.0-Large (same harness, its own runtime)
TypeSafe evalsafe documents	3030	57.3	90.9	74.1	68.8
law / rules / math (LegalBench, MMLU-Pro, GSM8K)	2295	74.4	90.1	82.2	84.7
code (CRUXEval, HumanEval, MBPP; execution-verified)	410	83.6	93.3	88.5	88.8
multilingual (Global-MMLU, Belebele; 10 languages)	500	68.5	89.2	78.9	81.6
hand-written hard rules (fictional statutes; held out)	100	68.4	69.6	69.0	70.1

Single forward pass, raw p50 latency 0.03 s on one H100 (vLLM).

Never trained on JevBench items or anything taken from the benchmark. Evaluation-only sources (LegalBench, MMLU-Pro, GSM8K, CRUXEval, HumanEval, MBPP, Global-MMLU, Belebele, TypeSafe evalsafe) were excluded from training and checked by 13-gram overlap.

Licence and credits

Apache-2.0. See NOTICE: Gemma 4 by Google (Apache-2.0); pplx-decider-v1.1-27b by Perplexity (Apache-2.0, used as a teacher); tasksource by Damien Sileo (per-dataset licences; non-commercial rows excluded).

Built by ContextBox AI.

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Safetensors

Model size

31B params

Tensor type

BF16

Model tree for contextboxai/Can-1.0-31B

Base model

google/gemma-4-31B

Finetuned

google/gemma-4-31B-it

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Quantizations

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