---
title: MobileMoE-L-Base
canonical_url: "https://www.modelscope.cn/models/facebook/MobileMoE-L-Base"
md_url: "https://www.modelscope.cn/models/facebook/MobileMoE-L-Base.md"
repository: facebook/MobileMoE-L-Base
last_updated: 2026-08-25
license: other
parameters: 5.3B
tensor_type:
  - BF16
downloads: 23
stars: 0
---

# MobileMoE-L-Base

> MobileMoE-L-Base - facebook 在 ModelScope 开源的模型。MobileMoE-L (Base) Model Card

facebook/MobileMoE-L-Base 是 ModelScope 魔搭社区上的 5.3B 参数机器学习模型，采用 other 许可。

- **Repository**: facebook/MobileMoE-L-Base
- **License**: other
- **Parameters**: 5.3B
- **Downloads**: 23
- **Stars**: 0
- **Last updated**: 2026-08-25

Source: https://www.modelscope.cn/models/facebook/MobileMoE-L-Base

---

# 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](https://huggingface.co/facebook/MobileMoE-S-Base) | [MobileMoE-M-Base](https://huggingface.co/facebook/MobileMoE-M-Base) | **MobileMoE-L-Base** |
| SFT | [MobileMoE-S-SFT](https://huggingface.co/facebook/MobileMoE-S-SFT) | [MobileMoE-M-SFT](https://huggingface.co/facebook/MobileMoE-M-SFT) | [MobileMoE-L-SFT](https://huggingface.co/facebook/MobileMoE-L-SFT) |
| QAT (INT4) | [MobileMoE-S-QAT](https://huggingface.co/facebook/MobileMoE-S-QAT) | [MobileMoE-M-QAT](https://huggingface.co/facebook/MobileMoE-M-QAT) | [MobileMoE-L-QAT](https://huggingface.co/facebook/MobileMoE-L-QAT) |

For the detailed technical report: 📝 [MobileMoE: Scaling On-Device Mixture of Experts](https://arxiv.org/abs/2605.27358)

For more versions, check out the 🤗 [MobileMoE Collection](https://huggingface.co/collections/facebook/mobilemoe)

![MobileMoE establishes a new Pareto frontier for on-device LLMs](mobilemoe_pareto.png)

**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 *F*<sub>inf</sub> = 2*N*<sub>act</sub> (GFLOPs) and (b) total parameters *N*<sub>total</sub> (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)<br>
**Active Parameters:** 922M<br>
**Total Parameters:** 5.3B<br>
**Layers:** 32<br>
**Model Dimension:** 1280<br>
**Attention Heads:** 20<br>
**KV Heads:** 4 (GQA)<br>
**Head Dimension:** 64<br>
**Routed Experts:** 60 (fine-grained, FFN hidden dim 640 each)<br>
**Active Experts per Token:** 4 (top-k sigmoid routing, with normalization)<br>
**Shared Expert:** 1, always on (FFN hidden dim 2560)<br>
**Vocabulary Size:** 128,256<br>
**Other Features:** QK-Norm, tied input/output embeddings, RoPE (θ = 500,000)<br>
**Input Modality:** Text<br>
**Output Modality:** Text<br>
**Languages:** English<br>
**Training Stages:** Pre-training → mid-training<br>
**Context Length:** 8,192 tokens<br>
**Precision:** BF16<br>
**Model Developer:** Meta<br>
**Model Release Date:** Aug 2026<br>
**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`](https://github.com/EleutherAI/lm-evaluation-harness); 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<sup>-4</sup>** | **4×10<sup>-5</sup>** | 4×10<sup>-6</sup> | 4×10<sup>-6</sup> |
| 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

```bash
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:

```python
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

```bibtex
@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](https://huggingface.co/facebook/MobileMoE-L-Base/blob/main/LICENSE).