Instructions to use Kailune-AI/pjev-2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kailune-AI/pjev-2b with Transformers:

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

pipe = pipeline("text-generation", model="Kailune-AI/pjev-2b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Kailune-AI/pjev-2b")
model = AutoModelForCausalLM.from_pretrained("Kailune-AI/pjev-2b", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Kailune-AI/pjev-2b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kailune-AI/pjev-2b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kailune-AI/pjev-2b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Kailune-AI/pjev-2b

SGLang

How to use Kailune-AI/pjev-2b 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 "Kailune-AI/pjev-2b" \
    --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": "Kailune-AI/pjev-2b",
		"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 "Kailune-AI/pjev-2b" \
        --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": "Kailune-AI/pjev-2b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Kailune-AI/pjev-2b with Docker Model Runner:
```
docker model run hf.co/Kailune-AI/pjev-2b
```

Kailune-AI commited on 15 days ago

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@@ -16,6 +16,8 @@ library_name: transformers
 # pjev-2b
 A small typed-decision model. Give it evidence, a question and a fixed set of allowed answers; it
 returns a calibrated probability for every option in one forward pass. It never generates text, so
 there is nothing to parse and nothing to hallucinate.
@@ -33,6 +35,28 @@ tokenizer, and calibration file. No separate adapter, no separate base download.
 [Base model](https://huggingface.co/openbmb/MiniCPM5-2B) ·
 [JevBench](https://github.com/fstandhartinger/jevbench)
 ## Accuracy per parameter
 ![Accuracy against parameter count on the JevBench hard tier](pjev-accuracy-per-parameter.png)
@@ -89,7 +113,7 @@ was trained.
 pip install torch transformers
 hf download Kailune-AI/pjev-2b --local-dir pjev-2b
 cd pjev-2b
-python serve.py --model . --calib ./calib.json --port 8000 --device cuda
 ```
 GPU recommended: `--device cuda` (default).

 # pjev-2b
+**The strongest 2B Jev model on the JevBench leaderboard** (Capability 57.7, first among all 2B-and-smaller models, 2026-10-14).
 A small typed-decision model. Give it evidence, a question and a fixed set of allowed answers; it
 returns a calibrated probability for every option in one forward pass. It never generates text, so
 there is nothing to parse and nothing to hallucinate.
 [Base model](https://huggingface.co/openbmb/MiniCPM5-2B) ·
 [JevBench](https://github.com/fstandhartinger/jevbench)
+## The strongest 2B Jev model on the JevBench leaderboard
+pjev-2b is on the official [JevBench leaderboard](https://benchmarkheaven.com/jev-models) (board v1.7.46,
+2026-10-14) and is the highest-scoring model at 2B or below: **Capability Score 57.7** (Intelligence 29.0,
+Calibration 86.5), first of the 31 systems at 2B or smaller, ahead of the next best (Decision 2B, 54.8).
+- It outscores all four open kev checkpoints (0.5B to 8B), Laya, openJev Verdict and popular 4B-class open Jev
+  models (SemIf, local-jev, spark-s1).
+- It outscores 28 systems with 4B or more parameters in total, including kev 8B (3x its size) and JADE (27B,
+  about 11x its size).
+- It does this at $0.016 per 1,000 decisions, half of Jev's on the board, and about 62 ms for a three-question
+  request on one H100, with strong calibration out of the box (86.5, no temperature fitting).
+![pjev-2b is first among all 2B-and-smaller models on JevBench](pjev-chart-1-best-2b.png)
+![pjev-2b outscores popular open Jev models, including ones up to 11x larger](pjev-chart-2-beats-open-jev.png)
+Capability Score is the leaderboard's mean of Intelligence and Calibration. For context: larger models still
+score higher, and overall the entry is mid-table (91 of 162 open-weights systems). The official latency for this
+entry was timed on a CPU host; on a GPU (`--device cuda`, the default) it runs in tens of milliseconds, see
+Quick start. The entry was measured on a newer evaluation draw than some older entries.
 ## Accuracy per parameter
 ![Accuracy against parameter count on the JevBench hard tier](pjev-accuracy-per-parameter.png)
 pip install torch transformers
 hf download Kailune-AI/pjev-2b --local-dir pjev-2b
 cd pjev-2b
+python serve.py --model . --calib ./calib.json --port 8000
 ```
 GPU recommended: `--device cuda` (default).