File size: 2,126 Bytes
51d42b1 9f6c26e 51d42b1 9f6c26e 51d42b1 9f6c26e 51d42b1 9f6c26e 51d42b1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 |
wget https://repo.anaconda.com/miniconda/Miniconda3-py310_23.1.0-1-Linux-x86_64.sh bash Miniconda3-py310_23.1.0-1-Linux-x86_64.sh enter, enter, yes, defaults sudo reboot conda activate conda create -n alpaca python=3.10 conda activate alpaca export PATH="/home/ubuntu/miniconda3/envs/alpaca/bin:$PATH" sudo apt-get install git-lfs git lfs install git clone https://github.com/tatsu-lab/stanford_alpaca git clone https://huggingface.co/decapoda-research/llama-7b-hf #remember to edit the tokenizer_config.json from LLaMATokenizer to LlamaTokenizer git clone https://huggingface.co/8bit-coder/alpaca-7b-nativeEnhanced pip install sentencepiece pip install git+https://github.com/huggingface/transformers.git cd ./stanford_alpaca pip install -r requirements.txt cd .. torchrun --nproc_per_node=8 --master_port=3045 ./stanford_alpaca/train.py --model_name_or_path ./llama-7b-hf --data_path ./alpaca-7b-nativeEnhanced/training_files/alpaca-megaset-fixed.json --fp16 True --output_dir ./output_7b --num_train_epochs 3 --per_device_train_batch_size 2 --per_device_eval_batch_size 2 --gradient_accumulation_steps 16 --evaluation_strategy "no" --save_strategy "steps" --save_steps 200 --learning_rate 2e-5 --weight_decay 0. --warmup_ratio 0.03 --lr_scheduler_type "cosine" --logging_steps 1 --fsdp "full_shard auto_wrap" --fsdp_transformer_layer_cls_to_wrap 'LlamaDecoderLayer' --tf32 True # now, make sure with nano that convert-hf-to-pth-16b.py has proper paths to everything pip install -q datasets loralib sentencepiece pip install bitsandbytes python convert-hf-to-pth-16b.py git clone https://github.com/antimatter15/alpaca.cpp cd alpaca.cpp mkdir models cd .. mv consolidated.01.pth ./alpaca.cpp/models/consolidated.00.pth mv params.json ./alpaca.cpp/models/params.json mv output_13b/tokenizer.model ./alpaca.cpp/models/tokenizer.model cd alpaca.cpp make cd .. python .deez/convert-pth-to-ggml.py ./alpaca.cpp/models 2 (1 for 7b, 2 for 13b, and the rest you can check yourself ;) cd alpaca.cpp ./quantize models/ggml-model-f16.bin ggml-alpaca-13b-nativeEnhanced-q4.bin 2 there's your finished model! |