e5-mistral-7b-instruct

docker build -t pytorch .

docker run --gpus=all --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 -it --rm -v $(pwd):/e5-mistral-7b-instruct/ pytorch bash

Prepare data

Run prepare_dataset to create a similarity dataset with one postive and negative pair from SNLI.

python prepare_dataset.py

Run model

set the model cache folder export TRANSFORMERS_CACHE=.cache/

First, run accelerate config --config_file ds_zero3_cpu.yaml

check the sample file for Single GPU here

Below given parameter is taken from the paper for finetuning. Adjust accroding to your dataset and usecase.

accelerate launch \
    --config_file ds_zero3_cpu.yaml \
    peft_lora_embedding_semantic_search.py \
    --dataset_name similarity_dataset \
    --max_length 512 \
    --model_name_or_path intfloat/e5-mistral-7b-instruct \
    --per_device_train_batch_size 1 \
    --per_device_eval_batch_size 1 \
    --learning_rate 0.0001 \
    --weight_decay 0.01 \
    --max_train_steps 1000 \
    --gradient_accumulation_steps 2048 \
    --lr_scheduler_type linear \
    --num_warmup_steps 100 \
    --output_dir trained_model \
    --use_peft

loss function copied from here -> https://github.com/RElbers/info-nce-pytorch