README.md

---
license: apache-2.0
base_model: jetmoe/jetmoe-8b
tags:
- alignment-handbook
- generated_from_trainer
datasets:
- HuggingFaceH4/ultrachat_200k
- HuggingFaceH4/airoboros-3.2
- HuggingFaceH4/Code-Feedback
- HuggingFaceH4/orca-math-word-problems-200k
- HuggingFaceH4/SystemChat
- HuggingFaceH4/capybara
model-index:
- name: jetmoe-8b-sft
  results: []
---

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

Here's a quick example to get you started with JetMoE-8B-chat:

```python
import torch
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
# Initialize the model and tokenizer
model_name = "jetmoe/jetmoe-8b-chat"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, attn_implementation="eager", trust_remote_code=True)
# Check if a GPU is available and move the model to GPU if it is
if torch.cuda.is_available():
    model = model.cuda()
    print("Using GPU:", torch.cuda.get_device_name(torch.cuda.current_device()))
else:
    print("GPU is not available, using CPU instead.")
# Encode input context
messages = [
    {
        "role": "system",
        "content": "You are a friendly chatbot",
    },
    {"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
 ]
tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
print(tokenized_chat)
# If using a GPU, move the input IDs to the GPU
if torch.cuda.is_available():
    input_ids = tokenized_chat.cuda()
# Generate text
output = model.generate(input_ids, max_length=500, num_return_sequences=1, no_repeat_ngram_size=2)
# If the output is on the GPU, move it back to CPU for decoding
if torch.cuda.is_available():
    output = output.cpu()
# Decode the generated text
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)
```

## 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.