Instructions to use HaoranLiu/DPO-4B-LiteOS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HaoranLiu/DPO-4B-LiteOS with Transformers:

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

pipe = pipeline("image-text-to-text", model="HaoranLiu/DPO-4B-LiteOS")
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("HaoranLiu/DPO-4B-LiteOS")
model = AutoModelForMultimodalLM.from_pretrained("HaoranLiu/DPO-4B-LiteOS", 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 HaoranLiu/DPO-4B-LiteOS with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HaoranLiu/DPO-4B-LiteOS"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HaoranLiu/DPO-4B-LiteOS",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/HaoranLiu/DPO-4B-LiteOS

SGLang

How to use HaoranLiu/DPO-4B-LiteOS 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 "HaoranLiu/DPO-4B-LiteOS" \
    --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": "HaoranLiu/DPO-4B-LiteOS",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "HaoranLiu/DPO-4B-LiteOS" \
        --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": "HaoranLiu/DPO-4B-LiteOS",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use HaoranLiu/DPO-4B-LiteOS with Docker Model Runner:
```
docker model run hf.co/HaoranLiu/DPO-4B-LiteOS
```

DPO-4B-LiteOS

Offline trajectory-level DPO on top of HaoranLiu/SFT-4B-LiteOS (a Qwen3-VL-4B-Instruct computer-use agent), trained with cua-lite + slime on HaoranLiu/DPO-Qwen3-LiteOS (898 preference pairs from Lite.OSWorld).

Results — Lite.OSWorld `eval` split

Identical protocol for both rows: 332 evaluated tasks (the 369-task split minus those with exclude_reason), greedy (temperature=0), max_steps: 30, concurrency 16, group_size=1, all valid.

model	mean episode_return	success rate (`>= 1.0`)
`SFT-4B-LiteOS` (init)	0.3231	104/332 = 31.3%
`DPO-4B-LiteOS` (this)	0.3511	113/332 = 34.0%
delta	+0.0280 (+8.7% rel.)	+9 tasks (+2.7 pp, +8.7% rel.)

The SFT row is quoted from that model's own card; the DPO row was measured in this run (summary.json: num_valid 332, mean_episode_return 0.3510540459064028).

Per-domain pass rate for this model: vs_code 66.7% · thunderbird 64.3% · os 63.2% · gimp 62.5% · vlc 46.7% · chrome 41.9% · libreoffice_writer 36.4% · libreoffice_impress 31.9% · libreoffice_calc 21.7% · multi_apps 13.0%. multi_apps is 92 of the 332 tasks and the weakest bucket — the same concentration the SFT card reports.

Objective

Each trajectory is scored as the sum of its per-action log-probs, each action conditioned on its own protocol-rendered context (screenshots are context only, masked out of the loss):

L = -log sigmoid( beta * [ (S_pol(t+) - S_ref(t+)) - (S_pol(t-) - S_ref(t-)) ] )

beta = 0.1, lr 5e-7 cosine, 1 epoch, 4 pairs/optimizer step, reference model = the SFT init. 224 steps, TP=2 on 2×H100 with optimizer CPU offload.

Known limitation

48% of optimizer steps had grad_norm < 1e-3: the chosen/rejected scores separate within ~30 steps, after which w = beta·sigmoid(-beta·Delta) collapses and the update is ~zero. So the +0.028 came from roughly half of the nominal steps. The pairs (gpt-5.5 successes vs perturbed failures) are probably too easy to distinguish — a harder rejected set, or margin-based filtering of the pairs, is the next lever, not lr/beta.

Full provenance (exact commands, wandb run, sanity checks) in run_info.txt.

Usage

Serve with sglang and drive through cua-lite's qwen3_vl adapter:

uv run python scripts/rollout.py \
  --model-id Qwen/Qwen3-VL-4B-Instruct \
  --model-path <path to this checkpoint> \
  --env-id lite.osworld --splits eval \
  --config-path scripts/configs/qwen3_vl/default/lite.osworld.yaml

The qwen3_vl adapter/config is required — the model was trained on that history protocol's rendering (full_history_size=4, native-resolution screenshots).

Downloads last month: 7

Safetensors

Model size

4B params

Tensor type

BF16

Model tree for HaoranLiu/DPO-4B-LiteOS

Base model

Qwen/Qwen3-VL-4B-Instruct

Finetuned

HaoranLiu/SFT-4B-LiteOS

Finetuned

(4)

this model

HaoranLiu
/

DPO-4B-LiteOS

DPO-4B-LiteOS

Results — Lite.OSWorld `eval` split

Objective

Known limitation

Usage

Model tree for HaoranLiu/DPO-4B-LiteOS

Dataset used to train HaoranLiu/DPO-4B-LiteOS

DPO-4B-LiteOS

Results — Lite.OSWorld eval split

Objective

Known limitation

Usage

Model tree for HaoranLiu/DPO-4B-LiteOS

Dataset used to train HaoranLiu/DPO-4B-LiteOS

Results — Lite.OSWorld `eval` split