Instructions to use smoky1496/SpawnPoint-Coder-7B-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use smoky1496/SpawnPoint-Coder-7B-AWQ with Transformers:

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

pipe = pipeline("text-generation", model="smoky1496/SpawnPoint-Coder-7B-AWQ")
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("smoky1496/SpawnPoint-Coder-7B-AWQ")
model = AutoModelForCausalLM.from_pretrained("smoky1496/SpawnPoint-Coder-7B-AWQ", 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 smoky1496/SpawnPoint-Coder-7B-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "smoky1496/SpawnPoint-Coder-7B-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "smoky1496/SpawnPoint-Coder-7B-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/smoky1496/SpawnPoint-Coder-7B-AWQ

SGLang

How to use smoky1496/SpawnPoint-Coder-7B-AWQ 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 "smoky1496/SpawnPoint-Coder-7B-AWQ" \
    --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": "smoky1496/SpawnPoint-Coder-7B-AWQ",
		"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 "smoky1496/SpawnPoint-Coder-7B-AWQ" \
        --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": "smoky1496/SpawnPoint-Coder-7B-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use smoky1496/SpawnPoint-Coder-7B-AWQ with Docker Model Runner:
```
docker model run hf.co/smoky1496/SpawnPoint-Coder-7B-AWQ
```

SpawnPoint-Coder-7B

A 7B model that generates Godot 4 games. Fine-tuned for Spawn Point, an open-source autonomous game studio: you describe a game in plain English or Vietnamese, the model writes the game design as strict JSON, and Spawn Point compiles it into a playable Godot 4 project (scenes, GDScript components, levels, sprites) that runs on desktop or in the browser.

What it does inside the pipeline:

Design — game idea → game brief (genre, character, abilities, enemies, hazards, pickups, goal, feel).
Tweak — "jump 20% higher, add a dash, more enemies" → a precise patch of the game.
Edit an entity — "double its health and make it shoot fireballs" → component and parameter changes.
Translate — Vietnamese game ideas → English briefs.

4-bit AWQ, runs on a single 12 GB GPU. No NVIDIA GPU? Use the GGUF build with Ollama, llama.cpp or LM Studio (Mac, AMD, CPU).

Run it

docker run --gpus all -p 8000:8000 --ipc=host vllm/vllm-openai:v0.10.2 \
  --model smoky1496/SpawnPoint-Coder-7B-AWQ --served-model-name SpawnPoint-Coder-7B --max-model-len 16384 --gpu-memory-utilization 0.85

Then Spawn Point finds it automatically at http://localhost:8000/v1.

Evaluation

Held-out Spawn Point tasks (same prompts and JSON schemas as production) plus 30 human-written game ideas (English and Vietnamese, 153 checks):

benchmark	v1	v2 (this release)
held-out brief (exact match)	36%	88%
held-out tweak (exact match)	100%	98%
held-out entity (exact match)	100%	91%
held-out translate (exact match)	52%	97%
human-written prompts fully right	73%	87%
human-written prompts, checks passed	94%	97%

Limitations

Fine-tuned only for 2D platformers and 2D top-down games built with Spawn Point's component library. Other genres (3D, RPG systems, strategy, card games, racing…) and free-form Godot coding were not part of the training data, so expect results no better than a general 7B coder model there.
It writes Spawn Point's JSON formats; the GDScript itself comes from Spawn Point's tested component library, not from the model. Outside Spawn Point it is a general coding model with a narrow specialty.
Occasionally one detail of a request is missed (e.g. a pickup or one enemy type); Spawn Point's validator and normalizer catch most of these.

License

Apache 2.0 — see LICENSE and NOTICE.

Downloads last month: 57

Safetensors

Model size

8B params

Tensor type

I32

BF16

F16