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