Instructions to use Modujo-AI/ModujoMoE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Modujo-AI/ModujoMoE with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Modujo-AI/ModujoMoE")
model = AutoModelForCausalLM.from_pretrained("Modujo-AI/ModujoMoE", 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 Modujo-AI/ModujoMoE with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Modujo-AI/ModujoMoE

SGLang

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

Docker Model Runner
How to use Modujo-AI/ModujoMoE with Docker Model Runner:
```
docker model run hf.co/Modujo-AI/ModujoMoE
```

Modujo model weights

This repository keeps model variants in self-contained subdirectories. The repository name is retained for compatibility, but the repository root no longer contains model weights and must not be loaded directly.

Weight directories

Path	Model	Meaning	Status
`pretrain/Modujo-1B-A0.75B/`	Modujo-1B-A0.75B	Continued-pretraining base model: 1B total scale and A0.75B active scale	Available
Repository root	Historical 9B-A1B architecture metadata and shared tokenizer files	The original random-initialized 9B weight shards were removed; this is not a loadable model directory	No weights

There are currently no SFT, chat, RL, or looped-model weights in this repository. New variants should be published in their own named directories so their training stage and actual parameter size remain explicit.

Pretrained base model

pretrain/Modujo-1B-A0.75B/ is the released pretraining artifact.

Architecture: Qwen4ExpForCausalLM
Model size: 1B total scale
Active size: approximately A0.75B per token
36 layers with 8 routed experts per layer, top-2 routing, and one shared expert
Attention layout: repeating 3 Gated DeltaNet layers + 1 dense-attention layer
Context configuration: 32K maximum positions; training sequences were up to 2,048 tokens
Weight format: BF16 safetensors
Training stage: continued-pretraining base model
Not instruction-tuned and not intended to be treated as a chat model
No QSA sparse indexer in this release

Detailed machine-readable size metadata is recorded in parameter_summary.json.

Loading

Pass the subdirectory explicitly:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "Alexhu1999/Modujo-9B-A1B"
subfolder = "pretrain/Modujo-1B-A0.75B"

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

Loading only Alexhu1999/Modujo-9B-A1B without subfolder will fail because there are intentionally no weights at the repository root.

Planned experiments

The current base model will be evaluated through three separate tracks:

SFT: improve instruction following, response quality, repetition control, and multi-turn dialogue stability.
QSA: add and train sparse attention indexers, then compare long-context quality, inference speed, and memory use against dense attention.
Looped Transformer: reuse selected Transformer layers to test whether deeper computation with shared parameters provides a practical quality and efficiency benefit.

These tracks will be evaluated independently before any combined model is considered. Future weights will use separate directories with explicit names.

Limitations

This is a base language model checkpoint. It can perform short text completion, but long generations may repeat and instruction following is not yet stable. Use a separately identified SFT or aligned release for assistant/chat use when one becomes available.

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