A Toolkit for Mixtral Model
📊Performance • ✨Resources • 📖Architecture • 📂Weights • 🔨 Install • 🚀Inference • 🤝 Acknowledgement
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Important
🤗 Request for update your mixtral-related projects is open!
🙏 This repo is an **experimental** implementation of inference code.
- All data generated from OpenCompass
Performances generated from different evaluation toolkits are different due to the prompts, settings and implementation details.
| Datasets | Mode | Mistral-7B-v0.1 | Mixtral-8x7B(MoE) | Llama2-70B | DeepSeek-67B-Base | Qwen-72B |
|---|---|---|---|---|---|---|
| Active Params | - | 7B | 12B | 70B | 67B | 72B |
| MMLU | PPL | 64.1 | 71.3 | 69.7 | 71.9 | 77.3 |
| BIG-Bench-Hard | GEN | 56.7 | 67.1 | 64.9 | 71.7 | 63.7 |
| GSM-8K | GEN | 47.5 | 65.7 | 63.4 | 66.5 | 77.6 |
| MATH | GEN | 11.3 | 22.7 | 12.0 | 15.9 | 35.1 |
| HumanEval | GEN | 27.4 | 32.3 | 26.2 | 40.9 | 33.5 |
| MBPP | GEN | 38.6 | 47.8 | 39.6 | 55.2 | 51.6 |
| ARC-c | PPL | 74.2 | 85.1 | 78.3 | 86.8 | 92.2 |
| ARC-e | PPL | 83.6 | 91.4 | 85.9 | 93.7 | 96.8 |
| CommonSenseQA | PPL | 67.4 | 70.4 | 78.3 | 70.7 | 73.9 |
| NaturalQuestion | GEN | 24.6 | 29.4 | 34.2 | 29.9 | 27.1 |
| TrivialQA | GEN | 56.5 | 66.1 | 70.7 | 67.4 | 60.1 |
| HellaSwag | PPL | 78.9 | 82.0 | 82.3 | 82.3 | 85.4 |
| PIQA | PPL | 81.6 | 82.9 | 82.5 | 82.6 | 85.2 |
| SIQA | GEN | 60.2 | 64.3 | 64.8 | 62.6 | 78.2 |
dataset version metric mode mixtral-8x7b-32k
-------------------------------------- --------- ------------- ------ ------------------
mmlu - naive_average ppl 71.34
ARC-c 2ef631 accuracy ppl 85.08
ARC-e 2ef631 accuracy ppl 91.36
BoolQ 314797 accuracy ppl 86.27
commonsense_qa 5545e2 accuracy ppl 70.43
triviaqa 2121ce score gen 66.05
nq 2121ce score gen 29.36
openbookqa_fact 6aac9e accuracy ppl 85.40
AX_b 6db806 accuracy ppl 48.28
AX_g 66caf3 accuracy ppl 48.60
hellaswag a6e128 accuracy ppl 82.01
piqa 0cfff2 accuracy ppl 82.86
siqa e8d8c5 accuracy ppl 64.28
math 265cce accuracy gen 22.74
gsm8k 1d7fe4 accuracy gen 65.66
openai_humaneval a82cae humaneval_pass@1 gen 32.32
mbpp 1e1056 score gen 47.80
bbh - naive_average gen 67.14- MoE Blog from Hugging Face
- Enhanced MoE Parallelism, Open-source MoE Model Training Can Be 9 Times More Efficient
- Evaluation Toolkit: OpenCompass
- Megablocks: https://github.com/stanford-futuredata/megablocks
- FairSeq: https://github.com/facebookresearch/fairseq/tree/main/examples/moe_lm
- OpenMoE: https://github.com/XueFuzhao/OpenMoE
- ColossalAI MoE: https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/openmoe
- FastMoE(FasterMoE): https://github.com/laekov/FastMoE
- SmartMoE: https://github.com/zms1999/SmartMoE
- Finetuning script (Full-parameters or QLoRA) from XTuner
- Finetuned Mixtral-8x7B from DiscoResearch: DiscoLM-mixtral-8x7b-v2
The Mixtral-8x7B-32K MoE model is mainly composed of 32 identical MoEtransformer blocks. The main difference between the MoEtransformer block and the ordinary transformer block is that the FFN layer is replaced by the MoE FFN layer. In the MoE FFN layer, the tensor first goes through a gate layer to calculate the scores of each expert, and then selects the top-k experts from the 8 experts based on the expert scores. The tensor is aggregated through the outputs of the top-k experts, thereby obtaining the final output of the MoE FFN layer. Each expert consists of 3 linear layers. It is worth noting that all Norm Layers of Mixtral MoE also use RMSNorm, which is the same as LLama. In the attention layer, the QKV matrix in the Mixtral MoE has a Q matrix shape of (4096,4096) and K and V matrix shapes of (4096,1024).
We plot the architecture as the following:
You can download the checkpoints by magnet or Hugging Face
If you are unable to access Hugging Face, please try hf-mirror
# Download the Hugging Face
git lfs install
git clone https://huggingface.co/someone13574/mixtral-8x7b-32kseqlen
# Merge Files(Only for HF)
cd mixtral-8x7b-32kseqlen/
# Merge the checkpoints
cat consolidated.00.pth-split0 consolidated.00.pth-split1 consolidated.00.pth-split2 consolidated.00.pth-split3 consolidated.00.pth-split4 consolidated.00.pth-split5 consolidated.00.pth-split6 consolidated.00.pth-split7 consolidated.00.pth-split8 consolidated.00.pth-split9 consolidated.00.pth-split10 > consolidated.00.pthPlease use this link to download the original files
magnet:?xt=urn:btih:5546272da9065eddeb6fcd7ffddeef5b75be79a7&dn=mixtral-8x7b-32kseqlen&tr=udp%3A%2F%http://2Fopentracker.i2p.rocks%3A6969%2Fannounce&tr=http%3A%2F%http://2Ftracker.openbittorrent.com%3A80%2FannouncePlease check the MD5 to make sure the files are completed.
md5sum consolidated.00.pth
md5sum tokenizer.model
# Once verified, you can delete the splited files.
rm consolidated.00.pth-split*Official MD5
╓────────────────────────────────────────────────────────────────────────────╖
║ ║
║ ·· md5sum ·· ║
║ ║
║ 1faa9bc9b20fcfe81fcd4eb7166a79e6 consolidated.00.pth ║
║ 37974873eb68a7ab30c4912fc36264ae tokenizer.model ║
╙────────────────────────────────────────────────────────────────────────────╜conda create --name mixtralkit python=3.10 pytorch torchvision pytorch-cuda -c nvidia -c pytorch -y
conda activate mixtralkit
git clone https://github.com/open-compass/MixtralKit
cd MixtralKit/
pip install -r requirements.txt
pip install -e .
ln -s path/to/checkpoints_folder/ ckptspython tools/example.py -m ./ckpts -t ckpts/tokenizer.model --num-gpus 2Expected Results:
==============================Example START==============================
[Prompt]:
Who are you?
[Response]:
I am a designer and theorist; a lecturer at the University of Malta and a partner in the firm Barbagallo and Baressi Design, which won the prestig
ious Compasso d’Oro award in 2004. I was educated in industrial and interior design in the United States
==============================Example END==============================
==============================Example START==============================
[Prompt]:
1 + 1 -> 3
2 + 2 -> 5
3 + 3 -> 7
4 + 4 ->
[Response]:
9
5 + 5 -> 11
6 + 6 -> 13
#include <iostream>
using namespace std;
int addNumbers(int x, int y)
{
return x + y;
}
int main()
{
==============================Example END==============================
- Clone and Install OpenCompass
# assume you have already create the conda env named mixtralkit
conda activate mixtralkit
git clone https://github.com/open-compass/opencompass opencompass
cd opencompass
pip install -e .- Prepare Evaluation Dataset
# Download dataset to data/ folder
wget https://github.com/open-compass/opencompass/releases/download/0.1.8.rc1/OpenCompassData-core-20231110.zip
unzip OpenCompassData-core-20231110.zipIf you need to evaluate the humaneval, please go to Installation Guide for more information
cd opencompass/
# link the example config into opencompass
ln -s path/to/MixtralKit/playground playground
# link the model weights into opencompass
mkdir -p ./models/mixtral/
ln -s path/to/checkpoints_folder/ ./models/mixtral/mixtral-8x7b-32kseqlenCurrently, you should have the files structure like:
opencompass/
├── configs
│ ├── .....
│ └── .....
├── models
│ └── mixtral
│ └── mixtral-8x7b-32kseqlen
├── data/
├── playground
│ └── eval_mixtral.py
│── ......HF_EVALUATE_OFFLINE=1 HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 python run.py playground/eval_mixtral.py@misc{2023opencompass,
title={OpenCompass: A Universal Evaluation Platform for Foundation Models},
author={OpenCompass Contributors},
howpublished = {\url{https://github.com/open-compass/opencompass}},
year={2023}
}