Instructions to use game2player/Game2Player-9B-Thinking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use game2player/Game2Player-9B-Thinking with Transformers:

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

pipe = pipeline("image-text-to-text", model="game2player/Game2Player-9B-Thinking")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("game2player/Game2Player-9B-Thinking")
model = AutoModelForMultimodalLM.from_pretrained("game2player/Game2Player-9B-Thinking", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use game2player/Game2Player-9B-Thinking with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "game2player/Game2Player-9B-Thinking"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "game2player/Game2Player-9B-Thinking",
		"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/game2player/Game2Player-9B-Thinking

SGLang

How to use game2player/Game2Player-9B-Thinking 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 "game2player/Game2Player-9B-Thinking" \
    --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": "game2player/Game2Player-9B-Thinking",
		"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 "game2player/Game2Player-9B-Thinking" \
        --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": "game2player/Game2Player-9B-Thinking",
		"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 game2player/Game2Player-9B-Thinking with Docker Model Runner:
```
docker model run hf.co/game2player/Game2Player-9B-Thinking
```

You need to agree to share your Hugging Face information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Game2Player-9B-Thinking

Final checkpoint after one epoch of SFT on game examples containing nonempty assistant thinking blocks. Training mixes DeepSeek and MiniMax data: 14,336 selected DeepSeek-v4.1-flash records and 8,138 MiniMax-m3.1-flash records. After preprocessing, 22,425 records remain (49 filtered). This is not a DeepSeek-only model. Final training step: 157.

Full language-model fine-tuning with vision encoder and aligner frozen; 4 H200 GPUs, BF16, ZeRO-2, gradient checkpointing, 25K packing, global batch 48, LR 1e-5, cosine schedule, 3% warmup, seed 3407. Base revision: c202236235762e1c871ad0ccb60c8ee5ba337b9a.

The 9B run uses non-fused AdamW. All 157 actual optimizer updates passed numerical checks. No downstream benchmark evaluation has been performed.

The repository contains inference weights and all tokenizer/processor assets, including supplemental base tokenizer/video assets where absent from the checkpoint. Optimizer states, RNG states, training data and images are not included. Full training checkpoints remain stored locally. See training_summary.json for provenance.

Load using a Transformers version supporting Qwen3.5 (the training environment uses Transformers 5.16.1). This repository is public; authentication is not required to download it.

import torch
from transformers import AutoProcessor, Qwen3_5ForConditionalGeneration

model_id = "game2player/Game2Player-9B-Thinking"
processor = AutoProcessor.from_pretrained(model_id)
model = Qwen3_5ForConditionalGeneration.from_pretrained(
    model_id, dtype=torch.bfloat16, device_map="auto"
)

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Safetensors

Model size

9B params

Tensor type

BF16

Model tree for game2player/Game2Player-9B-Thinking

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Qwen/Qwen3.5-9B-Base

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Qwen/Qwen3.5-9B

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