---
library_name: peft
tags:
- generated_from_trainer
base_model: JY623/KoSOLAR-v2.0
model-index:
- name: qlora-out/v1.2
  results: []
license: apache-2.0
---

<!-- 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. -->

[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.0`
```yaml
base_model: JY623/KoSOLAR-v2.0
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: true
strict: false

push_dataset_to_hub:
datasets:
  - path: kyujinpy/KOR-OpenOrca-Platypus-v3
    type: alpaca
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./qlora-out/v1.2

adapter: qlora
lora_model_dir:

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true

lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 2
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: false

warmup_steps: 20
evals_per_epoch: 4
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"
```

</details><br>

# qlora-out/v1.2

This model is a fine-tuned version of [JY623/KoSOLAR-v2.0](https://huggingface.co./JY623/KoSOLAR-v2.0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.1419

## 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: 0.0002
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 7
- gradient_accumulation_steps: 4
- total_train_batch_size: 28
- total_eval_batch_size: 7
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 20
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 13.4775       | 0.0   | 1    | 13.4330         |
| 6.9219        | 0.25  | 64   | 6.2022          |
| 5.5416        | 0.5   | 128  | 5.2780          |
| 5.4282        | 0.75  | 192  | 5.1929          |
| 5.4864        | 1.0   | 256  | 5.1416          |
| 5.2877        | 1.24  | 320  | 5.1441          |
| 5.1731        | 1.49  | 384  | 5.1413          |
| 5.6221        | 1.74  | 448  | 5.1406          |
| 5.3737        | 1.99  | 512  | 5.1419          |


### Framework versions

- PEFT 0.9.0
- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.0