Instructions to use 12kimih/Qwen3.5-4B-MBT-R with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 12kimih/Qwen3.5-4B-MBT-R with Transformers:

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

pipe = pipeline("text-generation", model="12kimih/Qwen3.5-4B-MBT-R")
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("12kimih/Qwen3.5-4B-MBT-R")
model = AutoModelForMultimodalLM.from_pretrained("12kimih/Qwen3.5-4B-MBT-R", 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 12kimih/Qwen3.5-4B-MBT-R with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "12kimih/Qwen3.5-4B-MBT-R"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "12kimih/Qwen3.5-4B-MBT-R",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/12kimih/Qwen3.5-4B-MBT-R

SGLang

How to use 12kimih/Qwen3.5-4B-MBT-R 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 "12kimih/Qwen3.5-4B-MBT-R" \
    --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": "12kimih/Qwen3.5-4B-MBT-R",
		"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 "12kimih/Qwen3.5-4B-MBT-R" \
        --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": "12kimih/Qwen3.5-4B-MBT-R",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use 12kimih/Qwen3.5-4B-MBT-R with Docker Model Runner:
```
docker model run hf.co/12kimih/Qwen3.5-4B-MBT-R
```

Qwen3.5-4B-MBT-R

Qwen/Qwen3.5-4B-Base fine-tuned on traces edited by MBT-R: the editor rewrites the whole trace in one call. It accompanies the paper "Distilling Metacognitive Behaviors through Agentic Editing".

Model	Training data
Qwen3.5-4B-Unedited-SFT	Unedited teacher traces
Qwen3.5-4B-MBT-R	MBT-R (rewrite)
Qwen3.5-4B-MBT-I	MBT-I (insert)
Qwen3.5-4B-MBT-A	MBT-A (agentic)

Training data: built from the traces in 12kimih/mbt-traces. Code and prompts: GitHub.

Usage

The chat template that ships with the model opens a <think> block, so the generated text is the reasoning, then </think> and the answer. End the question with the instruction used in training:

from vllm import LLM, SamplingParams

llm = LLM("12kimih/Qwen3.5-4B-MBT-R")
question = "Find the remainder when 2^100 is divided by 7."
prompt = question + "\n\nPlease reason step by step, and put your final answer within \\boxed{}."
params = SamplingParams(temperature=1.0, top_p=0.95, max_tokens=122880)
out = llm.chat([{"role": "user", "content": prompt}], params)
print(out[0].outputs[0].text)

The weights are bf16. Long answers are common, so leave room for tens of thousands of tokens.

Training

Setting	Value
Base model	Qwen/Qwen3.5-4B-Base
Method	Full-parameter supervised fine-tuning, loss on assistant tokens only
Epochs	15
Optimizer	Muon (Megatron implementation), learning rate 1e-5, weight decay 0
Schedule	Constant with 40 warmup steps
Global batch	64 sequences (8 GPUs x 8 accumulation steps), padding-free packing
Maximum length	131,072 tokens
Precision	bf16 autocast with fp32 master weights; released in bf16

License

Apache-2.0, following the base model. The training data is CC BY-SA 4.0; see the dataset card for upstream attribution.

Citation

@misc{kim2026distilling,
  title         = {Distilling Metacognitive Behaviors through Agentic Editing},
  author        = {Kim, Ik-hwan and Han, Hyeongrok and Jung, Mingi and Yu, Sangwon and Hong, Jinseok and Kim, Sang Hun and Choi, Yoonyoung and Yoon, Sungroh},
  year          = {2026},
  eprint        = {2602.22508},
  archivePrefix = {arXiv},
  primaryClass  = {cs.AI},
  url           = {https://arxiv.org/abs/2602.22508}
}

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Safetensors

Model size

5B params

Tensor type

BF16

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Base model

Qwen/Qwen3.5-4B-Base

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(260)

this model

Dataset used to train 12kimih/Qwen3.5-4B-MBT-R

Paper for 12kimih/Qwen3.5-4B-MBT-R

Mirroring the Mind: Distilling Human-Like Metacognitive Strategies into Large Language Models

Paper • 2602.22508 • Published Feb 26