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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

QGO-8B

QGO-8B is an OCR-centric GRPO checkpoint derived from 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

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.

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Safetensors

Model size

9B params

Tensor type

BF16

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QGO-8B