---
library_name: transformers
license: apache-2.0
base_model: llm-jp/llm-jp-3-3.7b-instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: sft
  results: []
language:
- ja
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Kendamarron/LongWriter-llm-jp-3-3.7b-instruct

[llm-jp/llm-jp-3-3.7b-instruct](https://huggingface.co/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
- [Kendamarron/Japanese-LongWriter-3k](https://huggingface.co/datasets/Kendamarron/Japanese-LongWriter-3k)

## 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
```