Instructions to use squaredcuber/Cybus-Qwen3-32B-v2-agentic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use squaredcuber/Cybus-Qwen3-32B-v2-agentic with Transformers:

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

pipe = pipeline("text-generation", model="squaredcuber/Cybus-Qwen3-32B-v2-agentic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("squaredcuber/Cybus-Qwen3-32B-v2-agentic")
model = AutoModelForCausalLM.from_pretrained("squaredcuber/Cybus-Qwen3-32B-v2-agentic")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use squaredcuber/Cybus-Qwen3-32B-v2-agentic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "squaredcuber/Cybus-Qwen3-32B-v2-agentic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "squaredcuber/Cybus-Qwen3-32B-v2-agentic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/squaredcuber/Cybus-Qwen3-32B-v2-agentic

SGLang

How to use squaredcuber/Cybus-Qwen3-32B-v2-agentic 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 "squaredcuber/Cybus-Qwen3-32B-v2-agentic" \
    --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": "squaredcuber/Cybus-Qwen3-32B-v2-agentic",
		"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 "squaredcuber/Cybus-Qwen3-32B-v2-agentic" \
        --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": "squaredcuber/Cybus-Qwen3-32B-v2-agentic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use squaredcuber/Cybus-Qwen3-32B-v2-agentic with Docker Model Runner:
```
docker model run hf.co/squaredcuber/Cybus-Qwen3-32B-v2-agentic
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Cybus-Qwen3-32B-v2-agentic

A 32B parameter model specialized for Roblox Luau code generation and agentic tool use in Roblox Studio environments.

This is an early experimental checkpoint in the Cybus series. A more capable successor is in development.

Model Details

Base model: Qwen/Qwen3-32B
Parameters: 32B
Context length: 32,768 tokens
Format: Full merged weights (bf16)
Language: English (instructions), Luau (code)
Intended use: Roblox Luau code generation, agentic tool-use in Roblox Studio

Usage

With `transformers`

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "squaredcuber/Cybus-Qwen3-32B-v2-agentic"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are a Roblox Studio Luau coding assistant."},
    {"role": "user", "content": "Write a script that spawns 10 red parts in a circle around the origin."},
]

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

outputs = model.generate(
    inputs,
    max_new_tokens=1024,
    temperature=0.3,
    top_p=0.9,
    do_sample=True,
)

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

With `vLLM`

vllm serve squaredcuber/Cybus-Qwen3-32B-v2-agentic \
  --max-model-len 32768 \
  --dtype bfloat16

Then call it via the OpenAI-compatible API on http://localhost:8000/v1.

Recommended sampling parameters

temperature: 0.3
top_p: 0.9
max_tokens: 1024–4096 depending on task

Prompt format

Uses the standard Qwen3 chat template. For best results on Luau coding tasks, use a concise system prompt like:

You are a Roblox Studio Luau coding assistant. Write complete, correct code using modern APIs.

Limitations

Specialized for Roblox Luau — not a general-purpose coding model.
Early experimental release. Code output quality varies, especially on complex multi-system game logic.
May occasionally emit deprecated Roblox APIs. Always review generated code before running in Studio.
Not trained for long-horizon agentic workflows beyond a few tool-call steps.

License

Released under the Apache 2.0 license with attribution required, inheriting from the base Qwen3-32B model.

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Safetensors

Model size

33B params

Tensor type

F16

Model tree for squaredcuber/Cybus-Qwen3-32B-v2-agentic

Base model

Qwen/Qwen3-32B

Finetuned

(496)

this model

Quantizations

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