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

Libraries

How to use khursheed/VibeGame-8B with Transformers:

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

pipe = pipeline("text-generation", model="khursheed/VibeGame-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("khursheed/VibeGame-8B")
model = AutoModelForCausalLM.from_pretrained("khursheed/VibeGame-8B", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use khursheed/VibeGame-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "khursheed/VibeGame-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": "khursheed/VibeGame-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/khursheed/VibeGame-8B

SGLang

How to use khursheed/VibeGame-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 "khursheed/VibeGame-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": "khursheed/VibeGame-8B",
		"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 "khursheed/VibeGame-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": "khursheed/VibeGame-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use khursheed/VibeGame-8B with Docker Model Runner:
```
docker model run hf.co/khursheed/VibeGame-8B
```

VibeGame-8B / code /scripts /extract_project.py

khursheed

Release pipeline source, tests, notebook, and licenses

ae36cfb verified 22 days ago

Raw History Blame Contribute Delete

615 Bytes

#!/usr/bin/env python3
"""Extract a strict generated file map into an empty project directory."""
import argparse
from pathlib import Path
import sys
sys.path.insert(0, str(Path(__file__).resolve().parents[1] / 'src'))
from vibegame.project import parse_project, write_project

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument('input', type=Path)
    parser.add_argument('--output', type=Path, required=True)
    args = parser.parse_args()
    target = write_project(parse_project(args.input.read_text()), args.output)
    print(target / 'project.godot')

	#!/usr/bin/env python3
	"""Extract a strict generated file map into an empty project directory."""
	import argparse
	from pathlib import Path
	import sys
	sys.path.insert(0, str(Path(__file__).resolve().parents[1] / 'src'))
	from vibegame.project import parse_project, write_project

	if __name__ == '__main__':
	parser = argparse.ArgumentParser(description=__doc__)
	parser.add_argument('input', type=Path)
	parser.add_argument('--output', type=Path, required=True)
	args = parser.parse_args()
	target = write_project(parse_project(args.input.read_text()), args.output)
	print(target / 'project.godot')