MobileMoE
Collection
a family of on-device MoE language models with sub-billion
active parameters (0.3-0.9B active and 1.3-5.3B total) that establish a new Pareto frontier • 10 items • Updated • 4
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MobileMoE-L (Base) Model Card
MobileMoE is a family of on-device Mixture-of-Experts (MoE) language models with sub-billion active parameters, designed to push the quality–efficiency Pareto frontier for on-device LLMs, including three model scales (S/M/L): 0.3B/0.5B/0.9B active parameters (1.3B/2.8B/5.3B total), with <3 GB INT4 weight footprints to fit in mobile DRAM. Each scale is released in three variants: a Base model (pre-training + mid-training), an SFT model (supervised fine-tuning), and a QAT model (quantization-aware training). You are currently in the MobileMoE-L-Base repository — the pre-trained 0.9B-active base model.
| S | M | L | |
|---|---|---|---|
| Active / total params | 272M / 1.3B | 528M / 2.8B | 922M / 5.3B |
| Layers | 20 | 26 | 32 |
| Model dimension | 768 | 1024 | 1280 |
| Heads (Q / KV) | 12 / 4 | 16 / 4 | 20 / 4 |
| Routed experts | 60 | 60 | 60 |
| Top-k | 4 | 4 | 4 |
| INT4 weight memory | 0.68 GB | 1.48 GB | 2.75 GB |
| Base | MobileMoE-S-Base | MobileMoE-M-Base | MobileMoE-L-Base |
| SFT | MobileMoE-S-SFT | MobileMoE-M-SFT | MobileMoE-L-SFT |
| QAT (INT4) | MobileMoE-S-QAT | MobileMoE-M-QAT | MobileMoE-L-QAT |
For the detailed technical report: 📝 MobileMoE: Scaling On-Device Mixture of Experts
For more versions, check out the 🤗 MobileMoE Collection
MobileMoE establishes a new Pareto frontier for on-device LLMs. Average benchmark accuracy, computed over 14 benchmarks spanning commonsense, knowledge, science, comprehension, and reasoning, is plotted against (a) per-token inference compute Finf = 2Nact (GFLOPs) and (b) total parameters Ntotal (B); in (b), x-axis tick labels show total params (B) | projected INT4 memory (GB).
Key Features
- A new Pareto frontier for on-device LLMs. Across 14 foundational benchmarks, MobileMoE matches or exceeds leading on-device dense LLMs at 2–4× fewer inference FLOPs, and matches or surpasses the state-of-the-art MoE OLMoE-1B-7B with up to 60% fewer parameters.
- Scaling-law-derived architecture. The architecture is derived from an on-device MoE scaling law that jointly optimizes under mobile memory and compute constraints, identifying an on-device sweet spot: moderate sparsity, with fine-grained experts and shared expert.
- Four-stage recipe. Pre-training → mid-training → instruction fine-tuning → INT4 quantization-aware training, all on open-source datasets.
Model Information
Model: MobileMoE-L-Base (pre-trained + mid-trained)
Active Parameters: 922M
Total Parameters: 5.3B
Layers: 32
Model Dimension: 1280
Attention Heads: 20
KV Heads: 4 (GQA)
Head Dimension: 64
Routed Experts: 60 (fine-grained, FFN hidden dim 640 each)
Active Experts per Token: 4 (top-k sigmoid routing, with normalization)
Shared Expert: 1, always on (FFN hidden dim 2560)
Vocabulary Size: 128,256
Other Features: QK-Norm, tied input/output embeddings, RoPE (θ = 500,000)
Input Modality: Text
Output Modality: Text
Languages: English
Training Stages: Pre-training → mid-training
Context Length: 8,192 tokens
Precision: BF16
Model Developer: Meta
Model Release Date: Aug 2026
License: MobileMoE is FAIR NC licensed
Results
All numbers below are for the base (pre-trained) models, re-evaluated under identical settings with greedy decoding using lm-eval; few-shot counts are given in parentheses after the benchmark name, and benchmarks shown without one are evaluated 0-shot.
Foundational benchmarks
| Capability | Benchmark | Gemma 3 1B | MobileLLM-Pro | Llama 3.2 1B | OLMo 2 1B | SmolLM2 1.7B | Qwen3.5 2B | OLMoE-1B-7B | MobileMoE-L |
|---|---|---|---|---|---|---|---|---|---|
| Active / total params | 1.0B | 1.1B | 1.2B | 1.5B | 1.7B | 1.9B | 1.3B / 6.9B | 922M / 5.3B | |
| Commonsense Reasoning | HellaSwag | 62.2 | 66.2 | 64.2 | 68.4 | 71.4 | 65.9 | 77.0 | 74.6 |
| PIQA | 74.9 | 76.6 | 75.1 | 75.9 | 77.6 | 74.7 | 80.5 | 80.0 | |
| SIQA | 42.9 | 48.4 | 42.8 | 44.0 | 44.2 | 43.5 | 43.9 | 54.3 | |
| WinoGrande | 58.8 | 63.2 | 61.3 | 65.0 | 66.1 | 64.6 | 69.1 | 68.2 | |
| Knowledge | MMLU (5-shot) | 26.2 | 32.3 | 31.4 | 42.4 | 50.2 | 54.1 | 52.6 | 59.6 |
| NaturalQuestions (5-shot) | 10.7 | 15.6 | 12.0 | 14.1 | 15.4 | 10.9 | 20.6 | 26.7 | |
| TriviaQA (5-shot) | 35.7 | 43.2 | 40.7 | 47.1 | 49.6 | 32.6 | 62.3 | 58.1 | |
| Science | ARC-Challenge (25-shot) | 39.3 | 52.5 | 40.3 | 45.2 | 53.3 | 54.3 | 55.0 | 57.0 |
| ARC-Easy | 72.2 | 76.6 | 61.8 | 73.4 | 73.4 | 71.4 | 76.6 | 81.7 | |
| OpenBookQA | 36.8 | 43.2 | 36.6 | 39.8 | 43.8 | 37.8 | 45.0 | 42.8 | |
| Reading | BoolQ | 66.6 | 77.5 | 63.6 | 62.9 | 72.4 | 69.4 | 74.8 | 75.7 |
| DROP (3-shot) | 23.4 | 22.5 | 27.9 | 34.6 | 27.3 | 53.6 | 29.8 | 64.7 | |
| Reasoning | BIG-Bench Hard (3-shot) | 30.5 | 33.0 | 29.8 | 33.3 | 34.0 | 48.2 | 33.5 | 37.8 |
| GSM8K (8-shot) | 2.3 | 6.6 | 7.3 | 38.6 | 31.0 | 65.3 | 12.3 | 55.7 | |
| Average | 41.6 | 47.0 | 42.5 | 48.9 | 50.7 | 53.3 | 52.4 | 59.8 |
Training
MobileMoE uses a four-stage recipe. This checkpoint is the output of stage 2 (mid-training).
| Pre-training | Mid-training | SFT | QAT | |
|---|---|---|---|---|
| Context length | 2,048 | 8,192 | 8,192 | 8,192 |
| Total tokens | ~6T | ~500B | ~126B | ~21B |
| Peak learning rate | 4×10-4 | 4×10-5 | 4×10-6 | 4×10-6 |
| LR schedule | Cosine | Linear | Cosine | Cosine |
| Token dispatch | drop-and-pad | drop-and-pad | dropless | dropless |
How to use
MobileMoE uses a custom architecture (model_type: mobilemoe) that is not yet part of upstream transformers, so trust_remote_code=True is required. The modeling code ships in this repo (configuration_mobilemoe.py, modeling_mobilemoe.py).
Requirements
pip install "torch>=2.1" "transformers>=4.57" "safetensors>=0.4" "accelerate>=1.0"
Verified with the following versions:
| Package | Version |
|---|---|
torch |
2.8.0 (cu128) |
transformers |
4.57.6 |
tokenizers |
0.22.2 |
safetensors |
0.7.0 |
accelerate |
1.13.0 |
For batch evaluation we recommend vLLM (≥ 0.10.2) with enforce_eager=True.
Text generation
This is a base model — it has no chat template and is not instruction-tuned. Prompt it with plain text continuation:
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
MODEL_ID = "facebook/MobileMoE-L-Base"
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
trust_remote_code=True,
dtype=torch.bfloat16,
)
model.to("cuda" if torch.cuda.is_available() else "cpu")
model.eval()
prompt = "Why are open-source on-device language models great?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
max_new_tokens=64,
do_sample=False,
temperature=None,
top_p=None,
pad_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Known issues. Loading the tokenizer on transformers 4.57.6 prints a fix_mistral_regex=True warning. Please ignore it and do not set the flag, as MobileMoE uses the Llama-3 tokenizer whose default tokenization is already correct.
Citation
@article{chen2026mobilemoe,
title={MobileMoE: Scaling On-Device Mixture of Experts},
author={Chen, Yanbei and Huang, Hanxian and Chang, Ernie and Szwejbka, Jacob and Desai, Digant and Liu, Zechun and Chandra, Vikas and Krishnamoorthi, Raghuraman},
journal={arXiv preprint arXiv:2605.27358},
year={2026}
}
License
MobileMoE is distributed under the FAIR Noncommercial Research License.
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