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https://github.com/myshell-ai/JetMoE

Reaching LLaMA2 Performance with 0.1M Dollars
https://github.com/myshell-ai/JetMoE

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Reaching LLaMA2 Performance with 0.1M Dollars

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# JetMoE: Reaching LLaMA2 Performance with 0.1M Dollars





  
 


   

   




## Key Messages



1. JetMoE-8B is **trained with less than $ 0.1 million**1 **cost but outperforms LLaMA2-7B from Meta AI**, who has multi-billion-dollar training resources. LLM training can be **much cheaper than people previously thought**.



2. JetMoE-8B is **fully open-sourced and academia-friendly** because:

    - It **only uses public datasets** for training, and the code is open-sourced. No proprietary resource is needed.

    - It **can be finetuned with very limited compute budget** (e.g., consumer-grade GPU) that most labs can afford.



3. JetMoE-8B **only has 2.2B active parameters** during inference, which drastically lowers the computational cost. Compared to a model with similar inference computation, like Gemma-2B, JetMoE-8B achieves constantly better performance.



1 We used a 96×H100 GPU cluster for 2 weeks, which cost ~$0.08 million.



Website: [https://research.myshell.ai/jetmoe](https://research.myshell.ai/jetmoe)



HuggingFace: [https://huggingface.co/jetmoe/jetmoe-8b](https://huggingface.co/jetmoe/jetmoe-8b)



Online Demo on Lepton AI: [https://www.lepton.ai/playground/chat?model=jetmoe-8b-chat](https://www.lepton.ai/playground/chat?model=jetmoe-8b-chat)



Technical Report: [https://arxiv.org/pdf/2404.07413.pdf](https://arxiv.org/pdf/2404.07413.pdf)



## Authors



The project is contributed by [Yikang Shen](https://scholar.google.com.hk/citations?user=qff5rRYAAAAJ), [Zhen Guo](https://zguo0525.github.io/), [Tianle Cai](https://www.tianle.website/#/) and [Zengyi Qin](https://www.qinzy.tech/). For technical inquiries, please contact [Yikang Shen](https://scholar.google.com.hk/citations?user=qff5rRYAAAAJ). For media and collaboration inquiries, please contact [Zengyi Qin](https://www.qinzy.tech/).



## Collaboration

**If you have great ideas but need more resources (GPU, data, funding, etc.)**, welcome to contact **MyShell.ai** via [Zengyi Qin](https://www.qinzy.tech/). **MyShell.ai** is open to collaborations and are actively supporting high-quality open-source projects.



## Benchmarks

We use the same evaluation methodology as in the Open LLM leaderboard. For MBPP code benchmark, we use the same evaluation methodology as in the LLaMA2 and Deepseek-MoE paper. The results are shown below:



|Model|Activate Params|Training Tokens|Open LLM Leaderboard Avg|ARC|Hellaswag|MMLU|TruthfulQA|WinoGrande|GSM8k|MBPP|HumanEval|

|---|---|---|---|---|---|---|---|---|---|---|---|

|Shot||||25|10|5|0|5|5|3|0|

|Metric||||acc_norm|acc_norm|acc|mc2|acc|acc|Pass@1|Pass@1|

|LLaMA2-7B|7B|2T|51.0|53.1|78.6|46.9|38.8|74|14.5|20.8|12.8|

|LLaMA-13B|13B|1T|51.4|**56.2**|**80.9**|47.7|39.5|**76.2**|7.6|22.0|15.8|

|DeepseekMoE-16B|2.8B|2T|51.1|53.2|79.8|46.3|36.1|73.7|17.3|34.0|**25.0**|

|Gemma-2B|2B|2T|46.4|48.4|71.8|41.8|33.1|66.3|16.9|28.0|24.4|

|JetMoE-8B|2.2B|1.25T|**53.0**|48.7|80.5|**49.2**|**41.7**|70.2|**27.8**|**34.2**|14.6|



| Model               | MT-Bench Score     |

|---------------------|-----------|

| GPT-4               | 9.014     |

| GPT-3.5-turbo       | 7.995     |

| Claude-v1           | 7.923     |

| **JetMoE-8B-chat**  | **6.681** |

| Llama-2-13b-chat    | 6.650     |

| Vicuna-13b-v1.3     | 6.413     |

| Wizardlm-13b        | 6.353     |

| Llama-2-7b-chat     | 6.269     |



To our surprise, despite the lower training cost and computation, JetMoE-8B performs even better than LLaMA2-7B, LLaMA-13B, and DeepseekMoE-16B. Compared to a model with similar training and inference computation, like Gemma-2B, JetMoE-8B achieves better performance.



## Model Usage

To load the models, you need install this package:

```

pip install -e .

```



Then you can load the model with the following code:

```python

from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, AutoModelForSequenceClassification

from jetmoe import JetMoEForCausalLM, JetMoEConfig, JetMoEForSequenceClassification



AutoConfig.register("jetmoe", JetMoEConfig)

AutoModelForCausalLM.register(JetMoEConfig, JetMoEForCausalLM)

AutoModelForSequenceClassification.register(JetMoEConfig, JetMoEForSequenceClassification)



tokenizer = AutoTokenizer.from_pretrained('jetmoe/jetmoe-8b')

model = AutoModelForCausalLM.from_pretrained('jetmoe/jetmoe-8b')

```



## Model Details

Please refer to the technical report [https://arxiv.org/pdf/2404.07413.pdf](https://arxiv.org/pdf/2404.07413.pdf) for model details and training details.



## Acknowledgement

We express our gratitude to [Shengding Hu](https://shengdinghu.github.io/) for his valuable advice on the Phase 2 data mixture. We also express our gratitude to [Exabits](https://www.exabits.ai/) for their assistance in setting up the GPU clusters.