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