---
library_name: peft
base_model: oopsung/llama2-7b-koNqa-test-v1
tags:
- axolotl
- generated_from_trainer
datasets:
- 7897b36af6847987_train_data.json
model-index:
- name: test-llama2-7b-koNqa-test-v1
  results: []
---

<!-- 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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.6.0`
```yaml
adapter: lora
base_model: oopsung/llama2-7b-koNqa-test-v1
bf16: auto
data_collator:
  max_length: 8192
  padding: true
  type: dynamic_padding
dataset_prepared_path: null
datasets:
- data_files:
  - 7897b36af6847987_train_data.json
  ds_type: json
  format: custom
  path: 7897b36af6847987_train_data.json
  preprocessing:
  - shuffle: true
  type:
    field: null
    field_input: null
    field_instruction: mood
    field_output: lyrics
    field_system: null
    format: null
    no_input_format: null
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: taopanda/test-llama2-7b-koNqa-test-v1
learning_rate: 0.0001980900647573094
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 600
micro_batch_size: 8
model_type: LlamaForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: ./outputs/lora-out/taopanda_test-llama2-7b-koNqa-test-v1
resume_from_checkpoint: null
s2_attention: null
save_safetensors: true
save_steps: 0.15
save_total_limit: 1
seed: 26232
special_tokens: null
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.1
wandb_entity: fatcat87-taopanda
wandb_log_model: null
wandb_mode: online
wandb_name: taopanda_test-llama2-7b-koNqa-test-v1
wandb_project: subnet56-test
wandb_runid: taopanda_test-llama2-7b-koNqa-test-v1
wandb_watch: null
warmup_ratio: 0.06
weight_decay: 0.0
xformers_attention: null

```

</details><br>

# test-llama2-7b-koNqa-test-v1

This model is a fine-tuned version of [oopsung/llama2-7b-koNqa-test-v1](https://huggingface.co/oopsung/llama2-7b-koNqa-test-v1) on the 7897b36af6847987_train_data.json dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5600

## 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: 0.0001980900647573094
- train_batch_size: 8
- eval_batch_size: 8
- seed: 26232
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- total_eval_batch_size: 32
- 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_steps: 12
- training_steps: 205

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.0028        | 0.0098 | 1    | 2.0533          |
| 1.7152        | 0.2543 | 26   | 1.6692          |
| 1.5552        | 0.5086 | 52   | 1.6252          |
| 1.648         | 0.7628 | 78   | 1.6017          |
| 1.5565        | 1.0098 | 104  | 1.5852          |
| 1.5165        | 1.2641 | 130  | 1.5732          |
| 1.5192        | 1.5183 | 156  | 1.5643          |
| 1.5389        | 1.7726 | 182  | 1.5600          |


### Framework versions

- PEFT 0.14.0
- Transformers 4.48.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0