Instructions to use susybaka228/rezak-coder-omega-67b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use susybaka228/rezak-coder-omega-67b with Transformers:

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

pipe = pipeline("text-generation", model="susybaka228/rezak-coder-omega-67b")
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("susybaka228/rezak-coder-omega-67b")
model = AutoModelForCausalLM.from_pretrained("susybaka228/rezak-coder-omega-67b", 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 susybaka228/rezak-coder-omega-67b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "susybaka228/rezak-coder-omega-67b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "susybaka228/rezak-coder-omega-67b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/susybaka228/rezak-coder-omega-67b

SGLang

How to use susybaka228/rezak-coder-omega-67b 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 "susybaka228/rezak-coder-omega-67b" \
    --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": "susybaka228/rezak-coder-omega-67b",
		"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 "susybaka228/rezak-coder-omega-67b" \
        --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": "susybaka228/rezak-coder-omega-67b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use susybaka228/rezak-coder-omega-67b with Docker Model Runner:
```
docker model run hf.co/susybaka228/rezak-coder-omega-67b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Rezak AI Coder OMEGA 67B

Developed by Semyaware Systems (Artificial Intelligence & Game Engine Division).

Rezak AI Coder OMEGA 67B is an enterprise-grade specialized causal language model engineered specifically for high-performance, secure, and production-ready Roblox Luau systems engineering.

Trained on curated, zero-redundancy engineering corpora, Rezak OMEGA strictly enforces modern industry standards: static typing (--!strict), modern physics constraints, memory lifecycle management, and server-authoritative netcode.

Key Capabilities & Architectural Pillars

Strict Luau Typing: Native enforcement of --!strict, exhaustive typed interfaces, and export types across modular game frameworks.
Modern Physics & Constraints Engine: Full support for AlignPosition, AlignOrientation, LinearVelocity, and VectorForce. Strict refusal and zero-tolerance for legacy deprecated BodyMovers (BodyVelocity, BodyPosition, BodyGyro, BodyAngularVelocity).
Server-Authoritative Netcode: Anti-cheat validation on server-side remotes, sanitization of client vectors/rates, and prevention of client-side trust exploits.
Memory Safety & Lifecycle Management: Strict usage of cleanup utilities (Trove, Maid), automated signal disconnection (RBXScriptConnection), and collision group management.
Enterprise Persistence: Robust implementations with ProfileService, session locking, reconciliation, and schema migration.

Quickstart / Transformers Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "susybaka228/rezak-coder-omega-67b"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

messages = [
    {"role": "user", "content": "Write a production-grade sprinting system with stamina and modern constraints in Luau."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=2048,
    temperature=0.2,
    top_p=0.95
)

print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

Model Specifications

Parameter	Specification
Developer	Semyaware Systems (AI & Game Engine Division)
Model Name	Rezak AI Coder OMEGA 67B
Architecture	Causal LM
Native Precision	FP16 (15.2 GB safetensors)
Primary Domain	Roblox Luau (Server, Client, Shared, Modules)
Official GGUF Quantizations	susybaka228/rezak-coder-omega-67b-GGUF

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Safetensors

Model size

8B params

Tensor type

F16

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Quantizations

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