Instructions to use sunweiwei/Ditto-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sunweiwei/Ditto-8B with Transformers:

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

pipe = pipeline("text-generation", model="sunweiwei/Ditto-8B")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("sunweiwei/Ditto-8B")
model = AutoModelForImageTextToText.from_pretrained("sunweiwei/Ditto-8B")
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

vLLM

How to use sunweiwei/Ditto-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sunweiwei/Ditto-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sunweiwei/Ditto-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sunweiwei/Ditto-8B

SGLang

How to use sunweiwei/Ditto-8B 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 "sunweiwei/Ditto-8B" \
    --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": "sunweiwei/Ditto-8B",
		"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 "sunweiwei/Ditto-8B" \
        --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": "sunweiwei/Ditto-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sunweiwei/Ditto-8B with Docker Model Runner:
```
docker model run hf.co/sunweiwei/Ditto-8B
```

Ditto-8B / README.md

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metadata

license: apache-2.0
base_model:
  - Qwen/Qwen3-VL-8B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
  - human-simulation
  - role-play
  - social-intelligence

Ditto-8B

Ditto-8B is an 8B open-weight model for human behavior simulation, covering theory of mind, character role-play, social skills, learner simulation, user simulation, and persona simulation.

📄 Paper: Reinforcing Human Behavior Simulation via Verbal Feedback
💻 Code: https://github.com/sunnweiwei/OdysSim
📊 Data (SOUL): https://huggingface.co/datasets/sunweiwei/Soul

Method

Ditto-8B is trained with DITTO, a reinforcement learning method that uses verbal feedback as the learning signal. After each output, the model receives descriptive feedback and produces an improved version; both are jointly optimized with GRPO. This distills the verbal guidance into the policy, so no feedback is needed at inference time.

Results

Primary metric for each benchmark (higher is better).

Dim	Benchmark	GPT 5.5	Gemini 3.1 Pro	Claude Opus 4.7	Qwen 3.6 Plus	Others*	Qwen3 8B Inst	Ditto-8B
CONV	UserLLM	65.3	67.7	57.6	72.1	44.6	46.0	91.5
CONV	MirrorBench	56.7	48.3	63.7	48.0	45.4	54.0	73.4
CONV	Humanual-Chat	28.2	21.0	22.6	22.2	25.8	24.7	21.0
CONV	SimArena-Doc	83.4	83.0	83.5	82.4	83.5	83.6	84.4
SS	Sotopia-Hard	31.9	27.8	32.4	28.3	31.7	27.7	45.8
COG	Fantom	93.0	93.0	80.0	89.0	70.0	23.0	92.0
COG	Hitom	82.0	86.0	93.0	73.0	56.0	62.0	79.0
COG	Paratomi	99.0	97.0	90.0	94.0	75.0	67.0	95.0
COG	Social-R1	69.0	79.0	67.0	67.0	47.0	54.0	50.0
ROLE	Coser	66.2	62.1	66.5	55.9	30.3	43.5	64.4
ROLE	Lifechoices	91.0	84.0	92.0	79.0	67.0	70.0	70.0
ROLE	Twinvoice	74.0	86.0	83.0	71.0	40.0	42.0	71.0
ROLE	BehaviorChain	95.0	92.0	96.0	85.0	36.0	41.0	44.0
ROLE	SimArena-Math	68.5	71.5	68.7	70.9	70.5	68.9	69.6
ROLE	Mistakes	72.0	73.0	74.0	67.0	56.0	27.0	36.0
ROLE	Humanual-Email	50.1	46.9	50.4	47.9	42.8	43.7	40.8
ROLE	Humanual-News	40.2	42.3	41.3	41.8	33.1	32.5	27.5
ROLE	Humanual-Politics	42.0	32.5	43.5	31.6	34.2	33.2	29.7
EVAL	AlignX	71.2	73.4	71.6	69.8	66.8	68.6	67.4
EVAL	Humanllm	45.7	46.9	44.2	42.7	35.2	34.1	33.1
EVAL	Socsci210	77.2	78.0	77.2	74.5	75.2	73.6	72.5
EVAL	Humanual-Book	57.6	62.4	61.4	58.4	50.2	53.6	53.4
EVAL	Humanual-Opinion	39.8	36.0	46.2	34.2	37.4	37.2	30.3

* Others: best result among other specialized human-simulation models (HumanLM-8B, Sotopia-RL-7B, UserLM-8B, Coser-8B).

Note. The released Ditto-8B is a single generalist distilled from a set of task-specific DITTO experts via rejection sampling on the training set.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "sunweiwei/Ditto-8B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")

messages = [{"role": "user", "content": "Hello!"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

Citation

@article{sun2026ditto,
  title         = {Reinforcing Human Behavior Simulation via Verbal Feedback},
  author        = {Sun, Weiwei and Zhou, Xuhui and Liu, Jiarui and Du, Weihua and Sun, Haojia and Xie, Yiqing and Ma, Qianou and Chen, Sihao and Wan, Mengting and Yang, Longqi and Zhou, Pei and Wu, Sherry and Welleck, Sean and Neubig, Graham and Yang, Yiming and Sap, Maarten},
  year          = {2026},
  eprint        = {2605.20506},
  archivePrefix = {arXiv},
  url           = {http://arxiv.org/abs/2605.20506}
}