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Former-commit-id: a4a2e94241bea6f96590f6cb8ca8b5cddee1917e
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hiyouga
2024-05-06 21:47:00 +08:00
parent 25de4ce56a
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35
examples/lora_single_gpu/dpo.sh
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#!/bin/bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train \
--stage dpo \
--do_train \
--model_name_or_path meta-llama/Llama-2-7b-hf \
--adapter_name_or_path ../../saves/LLaMA2-7B/lora/sft \
--create_new_adapter \
--dataset orca_rlhf \
--dataset_dir ../../data \
--template default \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--output_dir ../../saves/LLaMA2-7B/lora/dpo \
--overwrite_cache \
--overwrite_output_dir \
--cutoff_len 1024 \
--preprocessing_num_workers 16 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 8 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--warmup_steps 20 \
--save_steps 100 \
--eval_steps 100 \
--evaluation_strategy steps \
--load_best_model_at_end \
--learning_rate 1e-5 \
--num_train_epochs 1.0 \
--max_samples 1000 \
--val_size 0.1 \
--dpo_ftx 1.0 \
--plot_loss \
--fp16

39
examples/lora_single_gpu/llama3_lora_dpo.yaml Normal file
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# model
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
# method
stage: dpo
do_train: true
finetuning_type: lora
lora_target: q_proj,v_proj
dpo_ftx: 1.0
# dataset
dataset: orca_rlhf
template: llama3
cutoff_len: 1024
max_samples: 1000
val_size: 0.1
overwrite_cache: true
preprocessing_num_workers: 16
# output
output_dir: saves/llama3-8b/lora/dpo
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
# train
per_device_train_batch_size: 1
gradient_accumulation_steps: 8
learning_rate: 0.00001
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_steps: 0.1
fp16: true
# eval
per_device_eval_batch_size: 1
evaluation_strategy: steps
eval_steps: 500

19
examples/lora_single_gpu/llama3_lora_eval.yaml Normal file
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# model
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
adapter_name_or_path: saves/llama3-8b/lora/sft
# method
finetuning_type: lora
# dataset
task: mmlu
split: test
template: fewshot
lang: en
n_shot: 5
# output
save_dir: saves/llama3-8b/lora/eval
# eval
batch_size: 4

38
examples/lora_single_gpu/llama3_lora_orpo.yaml Normal file
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# model
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
# method
stage: orpo
do_train: true
finetuning_type: lora
lora_target: q_proj,v_proj
# dataset
dataset: orca_rlhf
template: llama3
cutoff_len: 1024
max_samples: 1000
val_size: 0.1
overwrite_cache: true
preprocessing_num_workers: 16
# output
output_dir: saves/llama3-8b/lora/orpo
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
# train
per_device_train_batch_size: 1
gradient_accumulation_steps: 8
learning_rate: 0.00001
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_steps: 0.1
fp16: true
# eval
per_device_eval_batch_size: 1
evaluation_strategy: steps
eval_steps: 500

38
examples/lora_single_gpu/llama3_lora_ppo.yaml Normal file
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# model
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
reward_model: saves/llama3-8b/lora/reward
# method
stage: ppo
do_train: true
finetuning_type: lora
lora_target: q_proj,v_proj
# dataset
dataset: identity,alpaca_gpt4_en
template: llama3
cutoff_len: 1024
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16
# output
output_dir: saves/llama3-8b/lora/ppo
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
# train
per_device_train_batch_size: 1
gradient_accumulation_steps: 8
learning_rate: 0.00001
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_steps: 0.1
fp16: true
# generate
max_new_tokens: 512
top_k: 0
top_p: 0.9

24
examples/lora_single_gpu/llama3_lora_predict.yaml Normal file
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# model
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
adapter_name_or_path: saves/llama3-8b/lora/sft
# method
stage: sft
do_predict: true
finetuning_type: lora
# dataset
dataset: identity,alpaca_gpt4_en
template: llama3
cutoff_len: 1024
max_samples: 50
overwrite_cache: true
preprocessing_num_workers: 16
# output
output_dir: saves/llama3-8b/lora/predict
overwrite_output_dir: true
# eval
per_device_eval_batch_size: 1
predict_with_generate: true

37
examples/lora_single_gpu/llama3_lora_pretrain.yaml Normal file
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# model
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
# method
stage: pt
do_train: true
finetuning_type: lora
lora_target: q_proj,v_proj
# dataset
dataset: c4_demo
cutoff_len: 1024
max_samples: 1000
val_size: 0.1
overwrite_cache: true
preprocessing_num_workers: 16
# output
output_dir: saves/llama3-8b/lora/sft
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
# train
per_device_train_batch_size: 1
gradient_accumulation_steps: 8
learning_rate: 0.0001
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_steps: 0.1
fp16: true
# eval
per_device_eval_batch_size: 1
evaluation_strategy: steps
eval_steps: 500

38
examples/lora_single_gpu/llama3_lora_reward.yaml Normal file
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# model
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
# method
stage: rm
do_train: true
finetuning_type: lora
lora_target: q_proj,v_proj
# dataset
dataset: orca_rlhf
template: llama3
cutoff_len: 1024
max_samples: 1000
val_size: 0.1
overwrite_cache: true
preprocessing_num_workers: 16
# output
output_dir: saves/llama3-8b/lora/reward
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
# train
per_device_train_batch_size: 1
gradient_accumulation_steps: 8
learning_rate: 0.00001
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_steps: 0.1
fp16: true
# eval
per_device_eval_batch_size: 1
evaluation_strategy: steps
eval_steps: 500

38
examples/lora_single_gpu/llama3_lora_sft.yaml Normal file
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# model
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
# method
stage: sft
do_train: true
finetuning_type: lora
lora_target: q_proj,v_proj
# dataset
dataset: identity,alpaca_gpt4_en
template: llama3
cutoff_len: 1024
max_samples: 1000
val_size: 0.1
overwrite_cache: true
preprocessing_num_workers: 16
# output
output_dir: saves/llama3-8b/lora/sft
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
# train
per_device_train_batch_size: 1
gradient_accumulation_steps: 8
learning_rate: 0.0001
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_steps: 0.1
fp16: true
# eval
per_device_eval_batch_size: 1
evaluation_strategy: steps
eval_steps: 500

22
examples/lora_single_gpu/llama3_preprocess.yaml Normal file
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# model
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
# method
stage: sft
do_train: true
finetuning_type: lora
lora_target: q_proj,v_proj
# dataset
dataset: identity,alpaca_gpt4_en
template: llama3
cutoff_len: 1024
max_samples: 1000
val_size: 0.1
overwrite_cache: true
preprocessing_num_workers: 16
tokenized_path: saves/llama3-8b/dataset/sft # use `tokenized_path` in config to load data
# output
output_dir: saves/llama3-8b/lora/sft
overwrite_output_dir: true

39
examples/lora_single_gpu/llava1_5_lora_sft.yaml Normal file
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# model
model_name_or_path: llava-hf/llava-1.5-7b-hf
visual_inputs: true
# method
stage: sft
do_train: true
finetuning_type: lora
lora_target: q_proj,v_proj
# dataset
dataset: mllm_demo
template: vicuna
cutoff_len: 1024
max_samples: 1000
val_size: 0.1
overwrite_cache: true
preprocessing_num_workers: 16
# output
output_dir: saves/llava1_5-7b/lora/sft
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
# train
per_device_train_batch_size: 1
gradient_accumulation_steps: 8
learning_rate: 0.0001
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_steps: 0.1
fp16: true
# eval
per_device_eval_batch_size: 1
evaluation_strategy: steps
eval_steps: 500

32
examples/lora_single_gpu/orpo.sh
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#!/bin/bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train \
--stage orpo \
--do_train \
--model_name_or_path meta-llama/Llama-2-7b-hf \
--dataset orca_rlhf \
--dataset_dir ../../data \
--template default \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--output_dir ../../saves/LLaMA2-7B/lora/orpo \
--overwrite_cache \
--overwrite_output_dir \
--cutoff_len 1024 \
--preprocessing_num_workers 16 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 8 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--warmup_steps 20 \
--save_steps 100 \
--eval_steps 100 \
--evaluation_strategy steps \
--load_best_model_at_end \
--learning_rate 1e-5 \
--num_train_epochs 1.0 \
--max_samples 1000 \
--val_size 0.1 \
--plot_loss \
--fp16

32
examples/lora_single_gpu/ppo.sh
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#!/bin/bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train \
--stage ppo \
--do_train \
--model_name_or_path meta-llama/Llama-2-7b-hf \
--adapter_name_or_path ../../saves/LLaMA2-7B/lora/sft \
--create_new_adapter \
--dataset alpaca_gpt4_en \
--dataset_dir ../../data \
--template default \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--reward_model ../../saves/LLaMA2-7B/lora/reward \
--output_dir ../../saves/LLaMA2-7B/lora/ppo \
--overwrite_cache \
--overwrite_output_dir \
--cutoff_len 512 \
--preprocessing_num_workers 16 \
--per_device_train_batch_size 1 \
--gradient_accumulation_steps 8 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--save_steps 100 \
--learning_rate 1e-5 \
--num_train_epochs 1.0 \
--max_samples 1000 \
--top_k 0 \
--top_p 0.9 \
--max_new_tokens 256 \
--plot_loss \
--fp16

19
examples/lora_single_gpu/predict.sh
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#!/bin/bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train \
--stage sft \
--do_predict \
--model_name_or_path meta-llama/Llama-2-7b-hf \
--adapter_name_or_path ../../saves/LLaMA2-7B/lora/sft,../../saves/LLaMA2-7B/lora/dpo \
--dataset alpaca_gpt4_en,glaive_toolcall \
--dataset_dir ../../data \
--template default \
--finetuning_type lora \
--output_dir ../../saves/LLaMA2-7B/lora/predict \
--overwrite_cache \
--overwrite_output_dir \
--cutoff_len 1024 \
--preprocessing_num_workers 16 \
--per_device_eval_batch_size 1 \
--max_samples 20 \
--predict_with_generate

19
examples/lora_single_gpu/prepare.sh
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#!/bin/bash
# use `--tokenized_path` in training script to load data
CUDA_VISIBLE_DEVICES= llamafactory-cli train \
--stage sft \
--do_train \
--model_name_or_path meta-llama/Llama-2-7b-hf \
--dataset alpaca_gpt4_en,glaive_toolcall \
--dataset_dir ../../data \
--template default \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--output_dir ../../saves/LLaMA2-7B/lora/sft \
--overwrite_cache \
--overwrite_output_dir \
--cutoff_len 1024 \
--preprocessing_num_workers 16 \
--max_samples 3000 \
--tokenized_path ../../saves/datasets/sft

31
examples/lora_single_gpu/pretrain.sh
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#!/bin/bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train \
--stage pt \
--do_train \
--model_name_or_path meta-llama/Llama-2-7b-hf \
--dataset c4_demo \
--dataset_dir ../../data \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--output_dir ../../saves/LLaMA2-7B/lora/pretrain \
--overwrite_cache \
--overwrite_output_dir \
--cutoff_len 1024 \
--preprocessing_num_workers 16 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 8 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--warmup_steps 20 \
--save_steps 100 \
--eval_steps 100 \
--evaluation_strategy steps \
--load_best_model_at_end \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--max_samples 10000 \
--val_size 0.1 \
--plot_loss \
--fp16

33
examples/lora_single_gpu/reward.sh
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#!/bin/bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train \
--stage rm \
--do_train \
--model_name_or_path meta-llama/Llama-2-7b-hf \
--adapter_name_or_path ../../saves/LLaMA2-7B/lora/sft \
--create_new_adapter \
--dataset orca_rlhf \
--dataset_dir ../../data \
--template default \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--output_dir ../../saves/LLaMA2-7B/lora/reward \
--overwrite_cache \
--overwrite_output_dir \
--cutoff_len 1024 \
--preprocessing_num_workers 16 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 8 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--warmup_steps 20 \
--save_steps 100 \
--eval_steps 100 \
--evaluation_strategy steps \
--learning_rate 1e-5 \
--num_train_epochs 1.0 \
--max_samples 5000 \
--val_size 0.1 \
--plot_loss \
--fp16

32
examples/lora_single_gpu/sft.sh
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#!/bin/bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train \
--stage sft \
--do_train \
--model_name_or_path meta-llama/Llama-2-7b-hf \
--dataset alpaca_gpt4_en,glaive_toolcall \
--dataset_dir ../../data \
--template default \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--output_dir ../../saves/LLaMA2-7B/lora/sft \
--overwrite_cache \
--overwrite_output_dir \
--cutoff_len 1024 \
--preprocessing_num_workers 16 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 8 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--warmup_steps 20 \
--save_steps 100 \
--eval_steps 100 \
--evaluation_strategy steps \
--load_best_model_at_end \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--max_samples 3000 \
--val_size 0.1 \
--plot_loss \
--fp16

33
examples/lora_single_gpu/sft_mllm.sh
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#!/bin/bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train \
--stage sft \
--do_train \
--model_name_or_path llava-hf/llava-1.5-7b-hf \
--visual_inputs \
--dataset mllm_demo \
--dataset_dir ../../data \
--template vicuna \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--output_dir ../../saves/LLaMA2-7B/lora/sft_mllm \
--overwrite_cache \
--overwrite_output_dir \
--cutoff_len 1024 \
--preprocessing_num_workers 16 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 8 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--warmup_steps 20 \
--save_steps 100 \
--eval_steps 100 \
--evaluation_strategy steps \
--load_best_model_at_end \
--learning_rate 5e-5 \
--num_train_epochs 100.0 \
--max_samples 3000 \
--val_size 0.1 \
--plot_loss \
--fp16