Instructions to use iamPi/game-v1.0.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use iamPi/game-v1.0.0 with Transformers:

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

pipe = pipeline("text-generation", model="iamPi/game-v1.0.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("iamPi/game-v1.0.0")
model = AutoModelForCausalLM.from_pretrained("iamPi/game-v1.0.0", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use iamPi/game-v1.0.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "iamPi/game-v1.0.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "iamPi/game-v1.0.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/iamPi/game-v1.0.0

SGLang

How to use iamPi/game-v1.0.0 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 "iamPi/game-v1.0.0" \
    --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": "iamPi/game-v1.0.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "iamPi/game-v1.0.0" \
        --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": "iamPi/game-v1.0.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use iamPi/game-v1.0.0 with Docker Model Runner:
```
docker model run hf.co/iamPi/game-v1.0.0
```

game-v1.0.0 / debug.log

iamPi

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	[2026-02-20 06:54:16,825] [WARNING] [py.warnings._showwarnmsg:110] [PID:284] /root/miniconda3/envs/py3.11/lib/python3.11/site-packages/torch/backends/__init__.py:46: UserWarning: Please use the new API settings to control TF32 behavior, such as torch.backends.cudnn.conv.fp32_precision = 'tf32' or torch.backends.cuda.matmul.fp32_precision = 'ieee'. Old settings, e.g, torch.backends.cuda.matmul.allow_tf32 = True, torch.backends.cudnn.allow_tf32 = True, allowTF32CuDNN() and allowTF32CuBLAS() will be deprecated after Pytorch 2.9. Please see https://pytorch.org/docs/main/notes/cuda.html#tensorfloat-32-tf32-on-ampere-and-later-devices (Triggered internally at /pytorch/aten/src/ATen/Context.cpp:80.)
	self.setter(val)

	Saving the dataset (0/3 shards): 0%\| \| 0/1000 [00:00<?, ? examples/s] Saving the dataset (1/3 shards): 33%\|███▎ \| 333/1000 [00:00<00:00, 3360.81 examples/s] Saving the dataset (1/3 shards): 67%\|██████▋ \| 667/1000 [00:00<00:00, 6613.40 examples/s] Saving the dataset (2/3 shards): 100%\|██████████\| 1000/1000 [00:00<00:00, 6613.40 examples/s] Saving the dataset (3/3 shards): 100%\|██████████\| 1000/1000 [00:00<00:00, 6613.40 examples/s] Saving the dataset (3/3 shards): 100%\|██████████\| 1000/1000 [00:00<00:00, 5759.67 examples/s]
	Loading checkpoint shards: 0%\| \| 0/2 [00:00<?, ?it/s] Loading checkpoint shards: 50%\|█████ \| 1/2 [00:00<00:00, 1.44it/s] Loading checkpoint shards: 100%\|██████████\| 2/2 [00:00<00:00, 2.43it/s] Loading checkpoint shards: 100%\|██████████\| 2/2 [00:00<00:00, 2.21it/s]
	[2026-02-20 06:54:39,046] [WARNING] [py.warnings._showwarnmsg:110] [PID:284] <string>:246: FutureWarning: The `max_prompt_length` argument is deprecated and will be removed in version 0.28.0. You should instead filter your dataset before training to ensure that prompts do not exceed your desired length.

	[2026-02-20 06:54:40,128] [WARNING] [py.warnings._showwarnmsg:110] [PID:284] /workspace/axolotl/src/axolotl/core/trainers/mixins/optimizer.py:209: UserWarning: You are importing from 'rollout_func', which is an experimental feature. This API may change or be removed at any time without prior notice. Silence this warning by setting environment variable TRL_EXPERIMENTAL_SILENCE=1.
	super().__init__(args, *kwargs)

	2026-02-20 06:55:21,445 - INFO - autotuner.py:256 - flashinfer.jit: [Autotuner]: Autotuning process starts ...
	2026-02-20 06:55:21,455 - INFO - autotuner.py:262 - flashinfer.jit: [Autotuner]: Autotuning process ends