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

Libraries

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

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

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HaoranLiu/SFT-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/SFT-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/SFT-4B-LiteOS

SGLang

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

SFT-4B-LiteOS

Qwen/Qwen3-VL-4B-Instruct supervised-finetuned on Lite.OSWorld desktop computer-use trajectories, trained with cua-lite.

The model takes a screenshot plus a task instruction and emits one Action: line followed by <tool_call> blocks against a computer_use tool (click / type / key / scroll / wait / terminate). It is trained with a 4-image sliding history window at 1000x1000 screen resolution.

License is inherited from the base model; see Qwen/Qwen3-VL-4B-Instruct.

Training data

Exported from cua-lite/Lite.OSWorld (desktop/use/train, the perturb + synth cohorts).


Source trajectories	2423
Filter	`not exclude_reason and episode_return > 0.5`
Kept	1967 trajectories
Teacher	`gpt-5.5`
Render config	`scripts/configs/qwen3_vl/default/lite.osworld.yaml` (4-image history, `max_steps: 30`)

Training

Megatron backend via slime v0.3.0, 4x H100 80GB.


Epochs	3 (1475 rollouts, batch 4)
LR	5e-6 cosine to 1e-6, warmup fraction 0.1
Parallelism	TP=2, PP=1, CP=1, EP=1 -> DP=2
Weight decay	0.1 (Adam, betas 0.9/0.95)
Loss	`sft_loss`, per-token
Checkpoint	`iter_1472` (final)

Evaluation

lite.osworld eval split, greedy (temperature=0), max_steps: 30, concurrency 16. The split holds 369 tasks; 37 carry an exclude_reason and are filtered out, leaving 332 evaluated tasks (all valid, group_size=1).

Metric	Value
Success rate (`episode_return >= 1.0`)	104/332 = 31.3%
Mean episode return	0.3231

Scores are near-binary: 224 zeros, 104 ones, and only 4 partial (0.536, 0.819, 0.903, 0.998).

Domain	Success	Rate
thunderbird	10/14	71.4%
vs_code	11/18	61.1%
os	10/19	52.6%
vlc	7/15	46.7%
gimp	7/16	43.8%
libreoffice_writer	9/22	40.9%
chrome	16/43	37.2%
libreoffice_impress	16/47	34.0%
libreoffice_calc	10/46	21.7%
multi_apps	8/92	8.7%

multi_apps is the largest bucket (28% of the eval set) and the weakest; excluding it the remaining domains average 40.0%. No base-model baseline was run, so these are absolute numbers rather than a measured delta.

Usage

from transformers import AutoProcessor, AutoModelForImageTextToText

model_id = "HaoranLiu/SFT-4B-LiteOS"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(model_id, device_map="auto")

Serving with SGLang:

python -m sglang.launch_server --model-path HaoranLiu/SFT-4B-LiteOS --port 30000

Downloads last month: 10

Safetensors

Model size

4B params

Tensor type

BF16

Model tree for HaoranLiu/SFT-4B-LiteOS

Base model

Qwen/Qwen3-VL-4B-Instruct

Finetuned

(446)

this model

Finetunes

4 models