# Mixtral ## Usage ### 1. Installation Please install the latest ColossalAI from source. ```bash CUDA_EXT=1 pip install -U git+https://github.com/hpcaitech/ColossalAI ``` Then install dependencies. ```bash cd ColossalAI/applications/ColossalMoE pip install -e . ``` Additionally, we recommend you to use torch 1.13.1. We've tested our code on torch 1.13.1 and found it's compatible with our code. ### 2. Inference Yon can use colossalai run to launch inference: ```bash bash infer.sh ``` If you already have downloaded model weights, you can change name to your weights position in `infer.sh`. ### 3. Train You first need to create `./hostfile`, listing the ip address of all your devices, such as: ```bash 111.111.111.110 111.111.111.111 ``` Then yon can use colossalai run to launch train: ```bash bash train.sh ``` It requires 16 H100 (80G) to run the training. The number of GPUs should be divided by 8. If you already have downloaded model weights, you can change name to your weights position in `train.sh`.
Mixtral

Usage

1. Installation

Please install the latest ColossalAI from source.
CUDA_EXT=1 pip install -U git+https://github.com/hpcaitech/ColossalAI
Then install dependencies.
cd ColossalAI/applications/ColossalMoE
pip install -e .
Additionally, we recommend you to use torch 1.13.1. We've tested our code on torch 1.13.1 and found it's compatible with our code.
2. Inference

Yon can use colossalai run to launch inference:
bash infer.sh
If you already have downloaded model weights, you can change name to your weights position in infer.sh.
3. Train

You first need to create ./hostfile, listing the ip address of all your devices, such as:
111.111.111.110
111.111.111.111
Then yon can use colossalai run to launch train:
bash train.sh
It requires 16 H100 (80G) to run the training. The number of GPUs should be divided by 8. If you already have downloaded model weights, you can change name to your weights position in train.sh.