MixtralKit
A Toolkit for Mixtral Model
English | ็ฎไฝไธญๆ
Click Github for infernece and evaluation.
Welcome to try OpenCompass for model evaluation, performance of Mixtral will be updated soon.
This repo is an experimental implementation of inference code, which is not officially released by Mistral AI.
Performance
Comparison with Other Models
- 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 | Llama2-70B | DeepSeek-67B-Base | Qwen-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 |
Performance Mixtral-8x7b
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
Prepare Model Weights
Download Weights
You can download the checkpoints by magnet or huggingface
HuggingFace
If you are unable to access huggingface, please try hf-mirror
# Download the huggingface
git lfs install
git clone https://huggingface.co/someone13574/mixtral-8x7b-32kseqlen
Magnet Link
Please 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%2Fannounce
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.pth
MD5 Validation
Please 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
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ โ
โ ยทยท md5sum ยทยท โ
โ โ
โ 1faa9bc9b20fcfe81fcd4eb7166a79e6 consolidated.00.pth โ
โ 37974873eb68a7ab30c4912fc36264ae tokenizer.model โ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
Install
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/ ckpts
Inference
Text Completion
python tools/example.py -m ./ckpts -t ckpts/tokenizer.model --num-gpus 2
Expected 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 dโOro 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==============================
Evaluation with OpenCompass
Step-1: Setup OpenCompass
- 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.zip
If you need to evaluate the humaneval, please go to Installation Guide for more information
Step-2: Pre-pare evaluation config and weights
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-32kseqlen
Currently, you should have the files structure like:
opencompass/
โโโ configs
โ โโโ .....
โ โโโ .....
โโโ models
โ โโโ mixtral
โ โโโ mixtral-8x7b-32kseqlen
โโโ data/
โโโ playground
โ โโโ eval_mixtral.py
โโโ ......
Step-3: Run evaluation experiments
HF_EVALUATE_OFFLINE=1 HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 python run.py playground/eval_mixtral.py