Instructions to use wenzani/Wendi-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wenzani/Wendi-2B with Transformers:

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

pipe = pipeline("text-generation", model="wenzani/Wendi-2B")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("wenzani/Wendi-2B")
model = AutoModelForMultimodalLM.from_pretrained("wenzani/Wendi-2B", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use wenzani/Wendi-2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wenzani/Wendi-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": "wenzani/Wendi-2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/wenzani/Wendi-2B

SGLang

How to use wenzani/Wendi-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 "wenzani/Wendi-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": "wenzani/Wendi-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 "wenzani/Wendi-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": "wenzani/Wendi-2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use wenzani/Wendi-2B with Docker Model Runner:
```
docker model run hf.co/wenzani/Wendi-2B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Wendi 2B

Fast, instruction-aware decisions from a compact 2B model.

Wendi is a System One decision model fine-tuned from Qwen3.5-2B. Given evidence, a decision criterion and a small set of alternatives, it selects an answer through a single next-token prediction.

Use cases include workflow routing, next-action selection, instruction-sensitive classification, evidence checks, tool-use analysis and email classification.

This repository contains the standalone merged BF16 checkpoint for wendi-2b.

Usage

Deploy with a recent SGLang or vLLM build supporting Qwen3.5. Install the engine for your GPU platform using its linked installation guide, then run one of the commands below from the downloaded model directory. Replace . with wenzani/Wendi-2B to load from Hugging Face instead.

SGLang

Installation guide

python -m sglang.launch_server \
  --model-path . \
  --served-model-name wendi-2b \
  --dtype bfloat16 \
  --tp-size 1 \
  --host 127.0.0.1 \
  --port 8000

vLLM

Installation guide · Qwen3.5 serving guide

vllm serve . \
  --served-model-name wendi-2b \
  --dtype bfloat16 \
  --tensor-parallel-size 1 \
  --language-model-only \
  --host 127.0.0.1 \
  --port 8000

Both expose an OpenAI-compatible API at http://127.0.0.1:8000/v1, with model name wendi-2b. For decision requests, set chat_template_kwargs to {"enable_thinking": false}, use uppercase option letters, and score the next token over the allowed alternatives. The configured context is 262,144 tokens; adjust SGLang's --context-length or vLLM's --max-model-len to fit your available memory.

Benchmarks

Internal held-out evaluations of Wendi 2B in BF16, using Transformers and restricted next-token option scoring on an AMD Radeon Pro W7900. All values below are accuracy; counts show correct answers out of evaluated examples. These measurements use the evaluation harness, not the serving engines above.

General decisions

Category	Correct / examples	Accuracy
Banking intent	131 / 160	81.88%
Contract evidence	101 / 160	63.12%
Emotion classification	122 / 160	76.25%
Multilingual intent	123 / 180	68.33%
Numeric reasoning	48 / 100	48.00%
Qualitative reasoning	94 / 100	94.00%
State tracking	93 / 100	93.00%
Temporal and numeric reasoning	33 / 60	55.00%
Workflow rules	70 / 100	70.00%
Overall	815 / 1,120	72.77%

Behavior analysis

Category	Correct / examples	Accuracy
Event order	57 / 60	95.00%
Latency checks	57 / 60	95.00%
Token-count metadata	53 / 60	88.33%
Tool-call detection	79 / 102	77.45%
Tool-call counting	74 / 99	74.75%
Exact user quotations	73 / 99	73.74%
Overall	393 / 480	81.88%

Email classification

Category	Correct / examples	Accuracy
Natural-email phishing	135 / 160	84.38%
Synthetic phishing contrasts	115 / 120	95.83%
Spam classification	76 / 80	95.00%

Instruction following

Two internal holdouts: the established suite has 120 examples per category; the additional scenario suite has 40 per category.

Category	Established suite	Additional scenarios
Constraint selection	66.67%	77.50%
Evidence scope	93.33%	92.50%
Exception policies	86.67%	90.00%
Numeric policies	62.50%	57.50%
Rule precedence	74.17%	87.50%
Untrusted-content handling	99.17%	100.00%
Overall	80.42% (579 / 720)	84.17% (202 / 240)

Training

Wendi was trained with supervised fine-tuning followed by hybrid GRPO, combining decision rewards, supervised loss and KL regularization. Language-only rank-16 LoRA adapters were merged into the BF16 checkpoint.

The final training stage used 4,000 examples over 1,000 optimizer updates:

Component	Examples
General-decision replay	1,600
Behavior analysis	800
Email classification	800
Instruction following	800

Data combines published datasets, synthetic tool-use conversations and original procedural tasks. Sources include:

Behavior and email: ToolACE (Apache-2.0; published synthetic conversations) and Champa, Rabbi and Zibran's curated email collection (CC-BY-4.0).
Intent and workflows: BANKING77, MASSIVE, CLINC and Mind2Web (CC-BY-4.0).
Evidence and reasoning: ContractNLI and QuaRTz (CC-BY-4.0), plus GSM8K (MIT).
Emotion and sentiment: GoEmotions (Apache-2.0) and TweetEval (CC-BY-3.0).

License

Apache-2.0. Wendi is a modified derivative of Qwen3.5-2B, incorporating supervised and hybrid-RL language-model updates. The upstream license is included in LICENSE. Training sources retain their original licenses and attribution.

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Safetensors

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Tensor type

BF16

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