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

Libraries

How to use DatasetMan/QGO-8B with Transformers:

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

pipe = pipeline("image-text-to-text", model="DatasetMan/QGO-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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("DatasetMan/QGO-8B")
model = AutoModelForMultimodalLM.from_pretrained("DatasetMan/QGO-8B", 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 DatasetMan/QGO-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DatasetMan/QGO-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": "DatasetMan/QGO-8B",
		"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/DatasetMan/QGO-8B

SGLang

How to use DatasetMan/QGO-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 "DatasetMan/QGO-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": "DatasetMan/QGO-8B",
		"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 "DatasetMan/QGO-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": "DatasetMan/QGO-8B",
		"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 DatasetMan/QGO-8B with Docker Model Runner:
```
docker model run hf.co/DatasetMan/QGO-8B
```

QGO-8B / README.md

DatasetMan

Publish PM4Bench v2 release artifact

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	---
	license: apache-2.0
	library_name: transformers
	pipeline_tag: image-text-to-text
	base_model: Qwen/Qwen3-VL-8B-Thinking
	tags:
	- qwen3-vl
	- vision-language
	- multilingual
	- ocr
	- grpo
	datasets:
	- DatasetMan/PM4Bench-QGO-Train
	---

	# QGO-8B

	QGO-8B is an OCR-centric GRPO checkpoint derived from
	[`Qwen/Qwen3-VL-8B-Thinking`](https://huggingface.co/Qwen/Qwen3-VL-8B-Thinking).
	This repository contains the complete global-step-200 BF16 checkpoint used in
	the PM4Bench paper.

	## Model details

	- Architecture: `Qwen3VLForConditionalGeneration`
	- Base model: `Qwen/Qwen3-VL-8B-Thinking`
	- Training method: GRPO
	- Released checkpoint: global step 200
	- Weight format: safetensors, four BF16 shards
	- Indexed tensor bytes: 17,534,247,392
	- License: Apache-2.0
	- Tested Transformers version: 4.57.6

	## Usage

	```python
	from transformers import AutoProcessor, Qwen3VLForConditionalGeneration

	model_id = "DatasetMan/QGO-8B"
	model = Qwen3VLForConditionalGeneration.from_pretrained(
	model_id,
	dtype="auto",
	device_map="auto",
	)
	processor = AutoProcessor.from_pretrained(model_id)
	```

	The full BF16 weights are approximately 17.5 GB before runtime allocations.
	Plan GPU/CPU memory for weights, vision inputs, KV cache, and generation in
	addition to the checkpoint size.

	Use the base model's official chat template and greedy decoding for PM4Bench
	evaluation. Task prompts and evaluation code are provided in
	<https://github.com/opendatalab/PM4Bench>.

	## Training

	The released data is available at
	<https://huggingface.co/datasets/DatasetMan/PM4Bench-QGO-Train>. The recipe
	uses 32 prompts and 8 rollouts per prompt (256 trajectories per step), AdamW
	with learning rate `1e-6`, BF16, and eight GPUs.

	## PM4Bench evaluation

	\| Model \| MDUR trad. \| MDUR vision \| MIQA trad. \| MIQA vision \| MSOCR \| MGUI \|
	\|---\|---:\|---:\|---:\|---:\|---:\|---:\|
	\| Qwen3-VL-8B-Thinking \| 38.55 \| 34.88 \| 53.63 \| 47.69 \| 1.53 \| 78.30 \|
	\| QGO-8B \| 46.82 \| 40.84 \| 55.24 \| 51.06 \| 8.17 \| 80.00 \|

	These are the audited paper results. MDUR and MGUI are percentages, MIQA is
	the six-dimension judge score on a 10-100 scale, and MSOCR is on a 0-40 scale.

	## Limitations

	QGO-8B targets multilingual OCR robustness. It inherits limitations and risks
	from the Qwen base model and is not guaranteed to improve every downstream
	task or language. Coordinate outputs, OCR transcriptions, and long-form
	reasoning should be validated before use in consequential applications.