Instructions to use Fu01978/SmolMoE-4x360M-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Fu01978/SmolMoE-4x360M-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="Fu01978/SmolMoE-4x360M-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Fu01978/SmolMoE-4x360M-Instruct")
model = AutoModelForCausalLM.from_pretrained("Fu01978/SmolMoE-4x360M-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 Fu01978/SmolMoE-4x360M-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Fu01978/SmolMoE-4x360M-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": "Fu01978/SmolMoE-4x360M-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Fu01978/SmolMoE-4x360M-Instruct

SGLang

How to use Fu01978/SmolMoE-4x360M-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 "Fu01978/SmolMoE-4x360M-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": "Fu01978/SmolMoE-4x360M-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 "Fu01978/SmolMoE-4x360M-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": "Fu01978/SmolMoE-4x360M-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Fu01978/SmolMoE-4x360M-Instruct with Docker Model Runner:
```
docker model run hf.co/Fu01978/SmolMoE-4x360M-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

SmolMoE-4x360M-Instruct

A Mixture-of-Experts model built by merging four SmolLM2-360M fine-tunes using mergekit. Each expert specializes in a distinct domain, with 2 experts active per token (~720M active parameters per forward pass out of ~1.4B total).

Experts

#	Model	Specialization
E0	HuggingFaceTB/SmolLM2-360M-Instruct	General knowledge, factual Q&A
E1	prithivMLmods/SmolLM2-CoT-360M	Chain-of-thought reasoning, logic
E2	summerstars/SolaraV2-coder-0517	Code generation, mathematics
E3	Fu01978/SmolLM2-360M-Instruct-Heretic	Creative writing, expressive language

Architecture

Base architecture: Mixtral-style MoE (via mergekit)
Total experts: 4
Active experts per token: 2
Gate mode: hidden (router trained on real hidden states), with subsequent router fine-tuning
Active parameters per token: ~720M

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
 
model = AutoModelForCausalLM.from_pretrained(
    "Fu01978/SmolMoE-4x360M-Instruct",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("Fu01978/SmolMoE-4x360M-Instruct")
 
messages = [{"role": "user", "content": "Implement a binary search in Python."}]
formatted = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(formatted, return_tensors="pt").to(model.device)
 
output = model.generate(**inputs, max_new_tokens=256, temperature=0.2, do_sample=True)
print(tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Limitations

General factual accuracy is imperfect — the model can hallucinate details on knowledge questions
At 360M per expert, complex multi-step reasoning has limits
E0 (General) is the weakest router target due to weight similarity with E3 (Heretic), which is a direct fine-tune of the same base

Created With

mergekit — MoE construction
Kaggle Dual T4 GPUs

Downloads last month: 28

Safetensors

Model size

1B params

Tensor type

BF16

Model tree for Fu01978/SmolMoE-4x360M-Instruct

Fu01978/SmolLM2-360M-Instruct-Heretic

HuggingFaceTB/SmolLM2-360M-Instruct

prithivMLmods/SmolLM2-CoT-360M

summerstars/SolaraV2-coder-0517

Merge model

this model

Quantizations

2 models