Instructions to use Alexhu1999/Modujo-9B-A1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Alexhu1999/Modujo-9B-A1B with Transformers:

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

pipe = pipeline("text-generation", model="Alexhu1999/Modujo-9B-A1B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Alexhu1999/Modujo-9B-A1B")
model = AutoModelForCausalLM.from_pretrained("Alexhu1999/Modujo-9B-A1B", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Alexhu1999/Modujo-9B-A1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Alexhu1999/Modujo-9B-A1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Alexhu1999/Modujo-9B-A1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Alexhu1999/Modujo-9B-A1B

SGLang

How to use Alexhu1999/Modujo-9B-A1B 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 "Alexhu1999/Modujo-9B-A1B" \
    --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": "Alexhu1999/Modujo-9B-A1B",
		"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 "Alexhu1999/Modujo-9B-A1B" \
        --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": "Alexhu1999/Modujo-9B-A1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Alexhu1999/Modujo-9B-A1B with Docker Model Runner:
```
docker model run hf.co/Alexhu1999/Modujo-9B-A1B
```

FlashNext-MoE-9B-A1B

Randomly initialized, language-only Qwen4ExpForCausalLM checkpoint.

Parameters: 9,000,082,560 (9.000082560B)
Approximate active backbone parameters per token: 0.995B
32 layers: 24 Gated DeltaNet and 8 Qwen Sparse Attention
64 routed experts, top-2, plus one shared expert per layer
No vision tower and no MTP head
BF16 weights in five safetensors shards
Qwen3.8-Flash-Next tokenizer and chat template

These weights are random and must be pretrained or distilled before useful inference.

from transformers import AutoModelForCausalLM, AutoTokenizer

path = "/path/to/flashnext-moe-9b/model"
tokenizer = AutoTokenizer.from_pretrained(path)
model = AutoModelForCausalLM.from_pretrained(path, dtype="bfloat16")

Downloads last month: 365

Safetensors

Model size

9B params

Tensor type

I64

BF16