Instructions to use DavidAU/Qwen3-24B-MOE-6x-4B-Star-Trek-AwayTeam-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DavidAU/Qwen3-24B-MOE-6x-4B-Star-Trek-AwayTeam-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="DavidAU/Qwen3-24B-MOE-6x-4B-Star-Trek-AwayTeam-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DavidAU/Qwen3-24B-MOE-6x-4B-Star-Trek-AwayTeam-Instruct")
model = AutoModelForCausalLM.from_pretrained("DavidAU/Qwen3-24B-MOE-6x-4B-Star-Trek-AwayTeam-Instruct")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use DavidAU/Qwen3-24B-MOE-6x-4B-Star-Trek-AwayTeam-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DavidAU/Qwen3-24B-MOE-6x-4B-Star-Trek-AwayTeam-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DavidAU/Qwen3-24B-MOE-6x-4B-Star-Trek-AwayTeam-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DavidAU/Qwen3-24B-MOE-6x-4B-Star-Trek-AwayTeam-Instruct

SGLang

How to use DavidAU/Qwen3-24B-MOE-6x-4B-Star-Trek-AwayTeam-Instruct 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 "DavidAU/Qwen3-24B-MOE-6x-4B-Star-Trek-AwayTeam-Instruct" \
    --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": "DavidAU/Qwen3-24B-MOE-6x-4B-Star-Trek-AwayTeam-Instruct",
		"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 "DavidAU/Qwen3-24B-MOE-6x-4B-Star-Trek-AwayTeam-Instruct" \
        --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": "DavidAU/Qwen3-24B-MOE-6x-4B-Star-Trek-AwayTeam-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DavidAU/Qwen3-24B-MOE-6x-4B-Star-Trek-AwayTeam-Instruct with Docker Model Runner:
```
docker model run hf.co/DavidAU/Qwen3-24B-MOE-6x-4B-Star-Trek-AwayTeam-Instruct
```

Qwen3-24B-MOE-6x-4B-Star-Trek-AwayTeam-Instruct

A fully gated INSTRUCT MOE (Mixture of Experts) model of 24B compressed into 18B "model size".

This is a "Colab" between myself and Nightmedia.

Gating is based on Star Trek characters NAME(s) that each model said it was closest to during testing (no quotes):

"Q Continuum"
"[Q]"
"Enterprise Computer"
"Quark"
"Picard"
"Sisko"
"Janeway"
"Garak"
"Martok"
"Spock"
"Sarek"
"Data"
"Seven of Nine"
"Kira"
"Odo"
"Dr Crusher"
"Bashir"
"Worf"
"Klingons"

Use like:

"Sisko, [prompt here]"

"Sisko, Kira and Worf [prompt here]"

You can use:

"Away-Team" to address all experts.

Each model is isolated from one another and controlled using prompts and/or activation of additional experts.

You can set experts from 1 to 6 with default of 2.

Features:

Six of the top Qwen3 4B models (each benchmarked) in one package.
2 experts activated (adjustable)
"programmable" model which features gating instructions embedded in prompts and/or system prompts.
256k context.

[more coming soon...]

Downloads last month: 3

Safetensors

Model size

18B params

Tensor type

BF16

Collections including DavidAU/Qwen3-24B-MOE-6x-4B-Star-Trek-AwayTeam-Instruct