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)

# pip install -U transformers accelerate
# 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=256)
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-AI commited on 15 days ago

Commit

a68462b

verified ·

1 Parent(s): 510566b

Index separate Modujo 4B PT and SFT releases

Browse files

Files changed (1) hide show

README.md +7 -5

README.md CHANGED Viewed

@@ -6,6 +6,8 @@ tags:
   - mixture-of-experts
   - language-model
   - pretrained
 ---
 # Modujo model weights
@@ -19,11 +21,11 @@ longer contains model weights and must not be loaded directly.
 | Path | Model | Meaning | Status |
 | --- | --- | --- | --- |
 | [`pretrain/Modujo-1B-A0.75B/`](./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
@@ -51,7 +53,7 @@ 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)
@@ -63,7 +65,7 @@ model = AutoModelForCausalLM.from_pretrained(
 )
 ```
-Loading only `Alexhu1999/Modujo-9B-A1B` without `subfolder` will fail because
 there are intentionally no weights at the repository root.
 ## Planned experiments

   - mixture-of-experts
   - language-model
   - pretrained
+  - pt
+  - sft
 ---
 # Modujo model weights
 | Path | Model | Meaning | Status |
 | --- | --- | --- | --- |
 | [`pretrain/Modujo-1B-A0.75B/`](./pretrain/Modujo-1B-A0.75B) | Modujo-1B-A0.75B | Continued-pretraining base model: 1B total scale and A0.75B active scale | Available |
+| [`sft/Modujo-4B-A0.66B/`](./sft/Modujo-4B-A0.66B) | Modujo-4B-A0.66B SFT | Intermediate short-data SFT checkpoint; teacher stage not started | Experimental |
+| [`pt/Modujo-4B-A0.66B/`](./pt/Modujo-4B-A0.66B) | Modujo-4B-A0.66B PT | Intermediate continued-pretraining snapshot; not instruction-tuned | 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 |
+Separate 4B PT and experimental SFT snapshots are available in named directories. The SFT has not completed its teacher-distillation stage. The PT is a later continuation snapshot than the SFT initialization source; see each model card for lineage and limitations.
 ## Pretrained base model
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
+repo_id = "Modujo-AI/ModujoMoE"
 subfolder = "pretrain/Modujo-1B-A0.75B"
 tokenizer = AutoTokenizer.from_pretrained(repo_id, subfolder=subfolder)
 )
 ```
+Loading only `Modujo-AI/ModujoMoE` without `subfolder` will fail because
 there are intentionally no weights at the repository root.
 ## Planned experiments