Instructions to use theaviv/Lev1-3.7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use theaviv/Lev1-3.7B with Transformers:

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

pipe = pipeline("text-generation", model="theaviv/Lev1-3.7B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("theaviv/Lev1-3.7B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use theaviv/Lev1-3.7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "theaviv/Lev1-3.7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "theaviv/Lev1-3.7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/theaviv/Lev1-3.7B

SGLang

How to use theaviv/Lev1-3.7B 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 "theaviv/Lev1-3.7B" \
    --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": "theaviv/Lev1-3.7B",
		"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 "theaviv/Lev1-3.7B" \
        --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": "theaviv/Lev1-3.7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use theaviv/Lev1-3.7B with Docker Model Runner:
```
docker model run hf.co/theaviv/Lev1-3.7B
```

Lev1 (K2-Horizon-3.7B)

Lev1 is a System One decision model, in the line of Typesafe AI's Jev: fast, intuitive calls on structured input. You send a state and typed questions, and Lev1 returns a probability for every option in one forward pass. It never generates text.

Fast. 28.8 ms median, 169.9 ms p95 per request on one RTX PRO 6000 Blackwell Server Edition.
Scored. 42.28 on Decision Index 0.2.1, from a complete run with the official harness.
Best in class on tools. 68.8 on Tools & Automation, above every model under 10B parameters on the board (as of 2026-09-28).
Small and open. A 3.7B model: a LoRA on IFM/K2-Horizon-3.7B, shipped as merged weights and as the adapter, under Apache-2.0.
Clean data. Training sources carry commercial-use licences and passed decontamination against the benchmark suite. The synthetic part is public as theaviv/lev1-decisions.

Training

I trained a LoRA (rank 32, alpha 64) on questions sampled from a 163,611-question mixture. Each short question also appears in a second option order, and a consistency loss pulls the two answers together. For 15,591 questions the target blends the gold label with Gemma-4-31B-it's probabilities, on sources where Gemma matched gold at least 60% of the time. MiMo-V2.6-Flash wrote the 4 LLM-written sets; I kept a text only when Qwen3-235B-A22B agreed with its label, and Gemma-4-31B-it too for all but the phishing emails.

Data	Licence	Role	Train questions
theaviv/lev1-decisions (17 generators, 4 LLM-written sets)	CC-BY-4.0; CC-BY-SA-3.0 (hallucination set)	synthetic	31,837
Open-Jev	CC0-1.0	general	43,311
multi_nli, snli, boolq, contract-nli, SemEval_NLI4CT, RAGTruth-processed	OANC/CC-BY/CC-BY-SA/MIT; CC-BY-SA-4.0; CC-BY-SA-3.0; CC-BY-4.0; MIT (annotations); Summary task only (news contexts)	inference	19,686
winogrande, social_i_qa, piqa, openbookqa, ai2_arc	Apache-2.0; CC-BY-4.0; AFL-3.0; CC-BY-SA-4.0	commonsense	18,315
esci, HelpSteer2, newyorker_caption_contest, chess-puzzles	Apache-2.0; CC-BY-4.0; CC0-1.0	mixed	16,950
clinc_oos, banking77, hwu_64, amazon_massive_intent	CC-BY-3.0; CC-BY-4.0	intent detection	13,061
esci	Apache-2.0	retrieval	12,280
gsm8k	MIT	reasoning	6,026
When2Call	CC-BY-4.0	tool use	2,145

Results

	Lev1	Decision 1.0 Lux	JPT-4B	Jet v6.2	Decider 4B	Kev 4B
Decision Index	42.28	43.49	43.04	42.60	40.70	34.64
Knowledge & Reasoning	22.7	30.9	28.7	28.7	25.7	22.9
Language Understanding	44.0	48.0	52.5	43.9	46.0	35.3
Retrieval & Classification	47.1	50.0	45.0	48.2	44.7	41.0
Tools & Automation	68.8	57.2	57.2	62.9	58.6	52.6
Arts & Human Taste	30.5	26.4	25.8	27.0	25.0	17.9

The other columns are entrants near Lev1 on the board. Scores are chance-corrected skill: 0 is guessing, 100 is perfect. The run answered all 150,759 requests; the harness scores 150,317 of them and excludes 442.

Try it

import sys
from huggingface_hub import snapshot_download

path = snapshot_download("theaviv/Lev1-3.7B")
sys.path.insert(0, path)  # the repo ships its scorer as the `lev1` package
from lev1.engine import Lev1Scorer

scorer = Lev1Scorer.from_pretrained(path)  # adapter=True loads the base plus the LoRA instead
probs = scorer.score(
    {"channel": "email", "message": "Checkout has returned a 500 error for an hour and orders are failing."},
    {
        "urgent": {"type": "noul", "instructions": "This message needs a reply within the hour."},
        "team": {
            "type": "choice",
            "instructions": "Which team should handle it?",
            "criteria": {"billing": "Payments, refunds and invoices.", "engineering": "Outages and bugs.", "sales": "Pricing and new accounts."},
        },
    },
)
print(probs["urgent"]["yes"], probs["team"])

Decision Index harness (bring your own copy of the suite):

PYTHONPATH="$(python -c 'from huggingface_hub import snapshot_download as d; print(d("theaviv/Lev1-3.7B"))')" python -m decision_index pipeline --engine lev1.engine:Lev1Engine --edition 0.2.1 --out runs/lev1

Limits

Two question types, choice and noul, up to 255 options. A request that does not fit raises Unsupported; Lev1 never truncates or drops an option.
States above 16,384 tokens take a slower chunked path.
English only. Weakest on Knowledge & Reasoning (22.7) and Arts & Human Taste (30.5).
The probabilities come straight from the softmax, without a fitted temperature. Check calibration on your data before you set thresholds.

Apache-2.0. Built on IFM/K2-Horizon-3.7B (Apache-2.0, revision fe504ef19c7e). By Aviv Dozorets.

Lev1 is not lev, the Interfaze entrant on the Decision Index board.

Downloads last month: 165

Safetensors

Model size

5B params

Tensor type

BF16

Model tree for theaviv/Lev1-3.7B

Base model

IFM/K2-Horizon-3.7B

Adapter

(1)

this model

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Lev1-3.7B