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language:
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library_name: transformers
tags:
- facebook
- meta
- pytorch
- mobilellm
🤗 Hugging Face | 📑 Paper | 💻 Code
Model Details
We present MobileLLM-R1, a new series of efficient reasoning models in the MobileLLM family. The release includes two categories of models:
Base models:
Final models:
Note: These models are not general-purpose chat models. They are Supervised Fine-Tuned (SFT) models, specifically trained to address mathematical, programming (Python, C++), and scientific problems.
In addition to the models, we release the complete training recipes and data sources to ensure reproducibility and support further research.
Remarkably, the MobileLLM-R1 950M, pre-trained on only ~2T high-quality tokens and with fewer than 5T total training tokens, achieves comparable or superior performance to Qwen3 0.6B, which was trained on 36T tokens, across MATH, GSM8K, MMLU, and LiveCodeBench benchmarks.
Compared to existing fully open-source models, MobileLLM-R1 950M model achieves ~5× higher accuracy on MATH compared to the Olmo 1.24B model and ~2× higher accuracy relative to the SmolLM2 1.7B model, despite being substantially smaller in parameter scale. In addition, MobileLLM-R1 950M outperforms both Olmo 1.24B and SmolLM2 1.7B by a wide margin on coding benchmarks, establishing a new state-of-the-art among fully open-source models.
News
- Sept 12, 2025: 🚀 MobileLLM-R1 models are released on HuggingFace.
- Spet 28, 2025: 🌟 The training code is also available on GitHub.
- Spet 29, 2025: 🎉 The technical report "MobileLLM-R1: Exploring the Limits of Sub-Billion Language Model Reasoners with Open Training Recipes" is also available! Please check it out.
Highlights
Pretrained Model
Token efficiency comparison across pretrained models
Post-trained Model
Model Architecture:
| # Layers | # Attnetion Heads | # KV Heads | Dim | Hidden Dim | Params | |
|---|---|---|---|---|---|---|
| MobileLLM-R1-140M | 15 | 9 | 3 | 576 | 2048 | 140M |
| MobileLLM-R1-360M | 15 | 16 | 4 | 1024 | 4096 | 359M |
| MobileLLM-R1-950M | 22 | 24 | 6 | 1536 | 6144 | 949M |
| Input modalities | Output modalities | Context Length | Vocaburary Size | Shared Embeddings | |
|---|---|---|---|---|---|
| MobileLLM-R1-140M-base | Text | Text | 4k | 128k | Yes |
| MobileLLM-R1-360M-base | Text | Text | 4k | 128k | Yes |
| MobileLLM-R1-950M-base | Text | Text | 4k | 128k | Yes |
| MobileLLM-R1-140M | Text | Text | 32k | 128k | Yes |
| MobileLLM-R1-360M | Text | Text | 32k | 128k | Yes |
| MobileLLM-R1-950M | Text | Text | 32k | 128k | Yes |
How to use
To load the pretrained model for further finetuning or evaluation:
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("facebook/MobileLLM-R1-950M")
model = AutoModelForCausalLM.from_pretrained("facebook/MobileLLM-R1-950M")
You can also run inference with vLLM. You only need to register the model architecture Llama4ForCausalLM with the vLLM ModelRegistry.
from vllm.model_executor.models.llama4 import Llama4ForCausalLM
from vllm.model_executor.models.registry import ModelRegistry
ModelRegistry.register_model("Llama4ForCausalLM", Llama4ForCausalLM)
Evaluation
MobileLLM-R1 base model
| Model | Size | MATH500 | GSM8K | MBPP | HumanEval | CommonSense Avg. | MMLU |
|---|---|---|---|---|---|---|---|
| 4-shot em |
8-shot em |
3-shot pass@1 |
0-shot pass@1 |
0-shot accuracy |
5-shot accuracy |
||
| <150M | |||||||
| SmolLM2-135M-base | 135M | 0.4 | 1.8 | 3.8 | 0.0 | 50.7 | -- |
| MobileLLM-R1-140M-base | 140M | 4.6 | 16.3 | 5.4 | 15.9 | 44.3 | -- |
| 150M - 400M | |||||||
| Gemma-3-270M-pt | 268M | 0.6 | 1.1 | 2.0 | 3.1 | 48.4 | 26.5 |
| SmolLM2-360M-base | 362M | 1.8 | 5.0 | 19.4 | 0.0 | 56.6 | 24.7 |
| MobileLLM-R1-360M-base | 359M | 13.4 | 39.4 | 20.8 | 32.9 | 51.0 | 26.8 |
| 400M - 1B | |||||||
| Qwen2.5-0.5B-base | 494M | 14.8 | 41.8 | 29.6 | 28.1 | 52.3 | 47.5 |
| Qwen3-0.6B-base | 596M | 29.8 | 60.9 | 39.0 | 30.5 | 55.3 | 52.4 |
| MobileLLM-R1-950M-base | 949M | 26.8 | 61.6 | 39.2 | 46.3 | 58.6 | 47.4 |
| > 1B | |||||||
| Gemma-3-1B-pt | 1.0B | 0.6 | 2.4 | 9.4 | 6.1 | 57.3 | 26.1 |
| LLaMA3.2-1B-base | 1.24B | 1.6 | 6.8 | 26.6 | 17.1 | 58.4 | 32.0 |
| OLMo-2-0425-1B-base | 1.48B | 5.2 | 39.8 | 7.8 | 6.7 | 61.0 | 42.4 |
| Qwen2.5-1.5B-base | 1.54B | 31.0 | 68.4 | 44.6 | 36.6 | 58.7 | 61.2 |
| SmolLM2-1.7B-base | 1.71B | 11.6 | 31.8 | 35.4 | 0.6 | 62.9 | 50.0 |
| Qwen3-1.7B-base | 2.03B | 38.5 | 76.2 | 56.4 | 47.6 | 60.9 | 62.1 |
Here, CommonSense Avg. denotes an average of 8 tasks in CommonSense Reasoning benchmarks including ARC-easy, ARC-challenge, BoolQ, PIQA, SIQA, HellaSwag, OBQA, and WinoGrand. Models with fewer than 150M parameters do not yield reliable MMLU scores and are therefore denoted as '—'.
MobileLLM-R1 post-trained model
| Model | Size | MATH500 | GSM8K | AIME'24 | AIME'25 | LiveCodeBench-v6 |
|---|---|---|---|---|---|---|
| 0-shot pass@1 |
0-shot pass@1 |
0-shot pass@1, n=64 |
0-shot pass@1, n=64 |
0-shot pass@1, n=16 |
||
| <150M | ||||||
| SmolLM2-135M-Instruct | 135M | 3.0 | 2.4 | -- | -- | 0.0 |
| MobileLLM-R1-140M | 140M | 6.2 | 4.1 | -- | -- | 1.7 |
| 150M - 400M | ||||||
| Gemma-3-270m-it | 268M | 6.8 | 8.4 | -- | -- | 0.0 |
| SmolLM2-360M-Instruct | 362M | 3.4 | 8.1 | -- | -- | 0.7 |
| MobileLLM-R1-360M | 359M | 28.4 | 24.5 | -- | -- | 5.1 |
| 400M - 1B | ||||||
| Qwen2.5-0.5B-Instruct | 494M | 31.2 | 48.1 | 0.1 | 0.3 | 3.6 |
| Qwen3-0.6B | 596M | 73.0 | 79.2 | 11.3 | 17.0 | 14.9 |
| MobileLLM-R1-950M | 949M | 74.0 | 67.5 | 15.5 | 16.3 | 19.9 |
| > 1B | ||||||
| Gemma-3-1B-it | 1.0B | 45.4 | 62.9 | 0.9 | 0.0 | 2.0 |
| LLaMA3.2-1B-Instruct | 1.24B | 24.8 | 38.8 | 1.1 | 0.2 | 4.1 |
| OLMo-2-0425-1B-Instruct | 1.48B | 19.2 | 69.7 | 0.6 | 0.1 | 0.0 |
| OpenReasoning-Nemotron-1.5B | 1.54B | 83.4 | 76.7 | 49.7 | 40.4 | 28.3 |
| DeepSeek-R1-Distill-Qwen-1.5B | 1.54B | 83.2 | 77.3 | 29.1 | 23.4 | 19.9 |
| Qwen2.5-1.5B-Instruct | 1.54B | 54.0 | 70.0 | 2.5 | 0.9 | 7.9 |
| SmolLM2-1.7B-Instruct | 1.71B | 19.2 | 41.8 | 0.3 | 0.1 | 4.4 |
| Qwen3-1.7B | 2.03B | 89.4 | 90.3 | 47.0 | 37.0 | 29.8 |
For AIME, we evaluate models across 64 runs and report the average accuracy. For LiveCodeBench, results are reported as the average accuracy across 16 runs. Models with fewer than 400M parameters do not produce reliable AIME scores and are therefore denoted as '—'.
Training
Training Process
Training stages and hyperparameter details
In the pretraining phase, MobileLLM-R1 models are randomly initialized and optimized using the Adam optimizer with hyperparameters (β_1, β_2, ε) = (0.9, 0.95, 1e-8), coupled with a weight decay coefficient of 0.1. The learning rate follows a 2k-step warmup schedule and then decays linearly from its peak to 10% of the maximum.
In the mid-training phase, we use Adam optimizer with learning rate linearly decays from its maximum value to zero. We employ knowledge distillation with Llama-3.1-8B-Instruct model as the teacher, where the student is trained via minimizing the KL divergence between its output logits and the teacher logits.
In the post-training phase, we use the Adam optimizer with zero weight decay. The learning rate warmup ratio is set to 0.03 for general-purpose SFT and 0.1 for reasoning-specific SFT, and it linearly decays from its maximum value to zero. Full training hyperparameters are provided in the table below.
| Stage | Phase | Tokens / Samples | BS | Sequence Length | Steps | LR | #GPUs | Training Time |
|---|---|---|---|---|---|---|---|---|
| Pre-training | Phase1 | 2T tokens | 16 | 2k | 500k | 4.00E-03 | 16 x 8 | 4-5 days |
| Phase2 | 2T tokens | 16 | 2k | 500k | 4.00E-03 | 16 x 8 | 4-5 days | |
| Mid-training | Phase1 | 100B tokens | 4 | 4k | 50K | 3.60E-04 | 16 x 8 | 1-2 days |
| Phase2 | 100B tokens | 4 | 4k | 50K | 3.60E-04 | 16 x 8 | 1-2 days | |
| Post-training | General SFT | 866K samples | 4 | 4k | 2 epochs | 5.00E-06 | 16 x 8 | ~2h |
| Reasoning SFT | 6.2M samples | 8 | 32k | 4 epochs | 8.00E-05 | 16 x 8 | ~2.5days |
Data Mix
Pre-training
| Dataset | Rows | Tokens (B) | Phase1 Mix Ratio | Phase2 Mix Ratio |
|---|---|---|---|---|
| StarCoder | 206,640,114 | 263.8 | 11.02% | 0.52% |
| OpenWebMath | 6,117,786 | 12.6 | 4.15% | 23.33% |
| FineWeb-Edu | 1,279,107,432 | 1300 | 65.63% | 54.83% |
| Wiki | 7,222,303 | 3.7 | 2.93% | 0.14% |
| Arxiv | 1,533,917 | 28 | 5.39% | 1.32% |
| StackExchange | 29,249,120 | 19.6 | 7.62% | 0.86% |
| Algebraic stack | 3,404,331 | 12.6 | 3.28% | 1.26% |
| Nemotron science | 708,920 | 2 | -- | 0.03% |
| Nemotron code | 10,108,883 | 16 | -- | 0.72% |
| Nemotron math | 22,066,397 | 15 | -- | 3.01% |
| Cosmopedia | 31,064,744 | 25 | -- | 2.70% |
| Facebook natural reasoning | 1,145,824 | 1.8 | -- | 3.18% |
| FineMath | 48,283,984 | 34 | -- | 8.01% |
| peS2o | 38,800,000 | 50 | -- | 0.08% |
| Total | 100% | 100% |
Mid-training
| Dataset | Subset | Rows (M) | Phase1 Mix Ratio | Phase2 Mix Ratio |
|---|---|---|---|---|
| Dolmino | DCLM Baseline | 606 | 37.03% | 6.51% |
| FLAN | 57.3 | 4.10% | 0.72% | |
| peS2o | 38.8 | 11.41% | 2.01% | |
| Wiki | 6.17 | 2.66% | 0.47% | |
| StackExchange | 2.48 | 2.12% | 2.00% | |
| Math | 21 | 11.63% | 29.10% | |
| Nemotron | Nemotron-Pretraining-Code-v1 | 882 | 20.69% | 29.10% |
| Nemotron-CC-Math-v1 | 144 | 3.45% | 19.40% | |
| StarCoder | StarCoder | 206 | 6.90% | 9.70% |
| Benchmark training set | TriviaQA (train) OBQA (train) NaturalQuestions (train) PIQA (train) GSM8K (train) BoolQ (train) ARC-Easy (train) ARC-Challenge (train) |
~0.01 | 0 | 0.97% |
| Total | 100.00% | 100.00% |
Post-training
| Phase | Dataset | Rows |
|---|---|---|
| General SFT | Tulu-3-sft-olmo-2-mixture-0225 | 866K samples |
| Reasoning SFT | OpenMathReasoning | 3.2M samples |
| OpenScienceReasoning-2 | 803K samples | |
| OpenCodeReasoning-2 | 2.16M samples |
Citation
If you find our model useful for your research, please consider citing:
@article{zhao2025mobilellm-r1,
title={MobileLLM-R1: Exploring the Limits of Sub-Billion Language Model Reasoners with Open Training Recipes},
author={Zhao, Changsheng and Chang, Ernie and Liu, Zechun and Chang, Chia-Jung and Wen, Wei and Lai, Chen and Cao, Sheng, and Tian, Yuandong and Krishnamoorthi, Raghuraman and Shi, Yangyang and Chandra, Vikas},
journal={arXiv preprint arXiv:2509.24945},
year={2025}
}
Contact
Changsheng Zhao, Meta Inc (cszhao at meta dot com)
Ernie Chang, Meta Inc (erniecyc at meta dot com)
Zechun Liu, Meta Inc (zechunliu at meta dot com)
License
MobileLLM is FAIR NC licensed as of now