Kendamarron/LongWriter-llm-jp-3-3.7b-instruct
llm-jp/llm-jp-3-3.7b-instructを長文出力ができるようにSFTしたモデルです。
Dataset
Detail
https://zenn.dev/kendama/articles/32aa9ec4bed409
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 8
- total_eval_batch_size: 4
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.7184 | 1.2626 | 500 | 0.7673 |
Framework versions
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
LLaMA-Factory yaml
### model
model_name_or_path: llm-jp/llm-jp-3-3.7b-instruct
### method
stage: sft
do_train: true
finetuning_type: full
deepspeed: examples/deepspeed/ds_z3_config.json
enable_liger_kernel: true
### dataset
dataset: longwriter
template: alpaca_ja
cutoff_len: 32768
overwrite_cache: true
preprocessing_num_workers: 16
### output
output_dir: saves/llm_jp/full/sft
logging_steps: 1
save_steps: 500
plot_loss: true
overwrite_output_dir: true
### train
per_device_train_batch_size: 2
gradient_accumulation_steps: 1
learning_rate: 1.0e-5
optim: adamw_bnb_8bit
num_train_epochs: 2.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: true
ddp_timeout: 180000000
### eval
val_size: 0.01
per_device_eval_batch_size: 1
eval_strategy: steps
eval_steps: 500
### logging
report_to: wandb