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---
license: apache-2.0
---
<div align="center">
  <div>&nbsp;</div>
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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**<sup>1</sup> **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.

<sup>1</sup> 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](https://github.com/myshell-ai/JetMoE):
```
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
JetMoE-8B has 24 blocks. 
Each block has two MoE layers: Mixture of Attention heads (MoA) and Mixture of MLP Experts (MoE).
Each MoA and MoE layer has 8 expert, and 2 experts are activated for each input token.
It has 8 billion parameters in total and 2.2B active parameters. 
JetMoE-8B is trained on 1.25T tokens from publicly available datasets, with a learning rate of 5.0 x 10<sup>-4</sup> and a global batch-size of 4M tokens.

<figure>
<center>
<img src="images/jetmoe_architecture.png" width="40%">
<figcaption>JetMoE Architecture</figcaption>
</center>
</figure>

## Training Details
Our training recipe follows the [MiniCPM](https://shengdinghu.notion.site/MiniCPM-Unveiling-the-Potential-of-End-side-Large-Language-Models-d4d3a8c426424654a4e80e42a711cb20?pvs=4)'s two-phases training method. Phase 1 uses a constant learning rate with linear warmup and is trained on 1 trillion tokens from large-scale open-source pretraining datasets, including RefinedWeb, Pile, Github data, etc. Phase 2 uses exponential learning rate decay and is trained on 250 billion tokens from phase 1 datasets and extra high-quality open-source datasets.

<figure>
<center>
<img src="images/Phase1_data.png" width="60%">
<img src="images/Phase2_data.png" width="60%">
</center>
</figure>

## Technical Report
For more details, please refer to the [JetMoE Technical Report](https://arxiv.org/abs/2404.07413).

## JetMoE Model Index
|Model|Index|
|---|---|
|JetMoE-8B-Base| [Link](https://huggingface.co/jetmoe/jetmoe-8B) |
|JetMoE-8B-SFT| [Link](https://huggingface.co/jetmoe/jetmoe-8B-sft) |
|JetMoE-8B-Chat| [Link](https://huggingface.co/jetmoe/jetmoe-8B-chat) |

## 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, and to [Lepton AI](https://www.lepton.ai/) for their support in setting up the chat demo.

	---
	license: apache-2.0
	---

	<div align="center">
	<div> </div>
	<img src="https://huggingface.co/proxy/cdn-uploads.huggingface.co/production/uploads/641de0213239b631552713e4/ieHnwuczidNNoGRA_FN2y.png" width="500"/>
	<img src="https://huggingface.co/proxy/cdn-uploads.huggingface.co/production/uploads/641de0213239b631552713e4/UOsk9_zcbHpCCy6kmryYM.png" width="530"/>
	</div>

	# JetMoE: Reaching LLaMA2 Performance with 0.1M Dollars

	## Key Messages

	1. JetMoE-8B is trained with less than $ 0.1 million<sup>1</sup> 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.

	<sup>1</sup> 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](https://github.com/myshell-ai/JetMoE):
	```
	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
	JetMoE-8B has 24 blocks.
	Each block has two MoE layers: Mixture of Attention heads (MoA) and Mixture of MLP Experts (MoE).
	Each MoA and MoE layer has 8 expert, and 2 experts are activated for each input token.
	It has 8 billion parameters in total and 2.2B active parameters.
	JetMoE-8B is trained on 1.25T tokens from publicly available datasets, with a learning rate of 5.0 x 10<sup>-4</sup> and a global batch-size of 4M tokens.

	<figure>
	<center>
	<img src="images/jetmoe_architecture.png" width="40%">
	<figcaption>JetMoE Architecture</figcaption>
	</center>
	</figure>

	## Training Details
	Our training recipe follows the [MiniCPM](https://shengdinghu.notion.site/MiniCPM-Unveiling-the-Potential-of-End-side-Large-Language-Models-d4d3a8c426424654a4e80e42a711cb20?pvs=4)'s two-phases training method. Phase 1 uses a constant learning rate with linear warmup and is trained on 1 trillion tokens from large-scale open-source pretraining datasets, including RefinedWeb, Pile, Github data, etc. Phase 2 uses exponential learning rate decay and is trained on 250 billion tokens from phase 1 datasets and extra high-quality open-source datasets.

	<figure>
	<center>
	<img src="images/Phase1_data.png" width="60%">
	<img src="images/Phase2_data.png" width="60%">
	</center>
	</figure>

	## Technical Report
	For more details, please refer to the [JetMoE Technical Report](https://arxiv.org/abs/2404.07413).

	## JetMoE Model Index
	\|Model\|Index\|
	\|---\|---\|
	\|JetMoE-8B-Base\| [Link](https://huggingface.co/jetmoe/jetmoe-8B) \|
	\|JetMoE-8B-SFT\| [Link](https://huggingface.co/jetmoe/jetmoe-8B-sft) \|
	\|JetMoE-8B-Chat\| [Link](https://huggingface.co/jetmoe/jetmoe-8B-chat) \|

	## 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, and to [Lepton AI](https://www.lepton.ai/) for their support in setting up the chat demo.