---
license: other
library_name: peft
tags:
- generated_from_trainer
base_model: allganize/Llama-3-Alpha-Ko-8B-Evo
model-index:
- name: data/models/llama3-alpha-ko-fmmlu-exp2
  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/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: `0.4.1`
```yaml
base_model: allganize/Llama-3-Alpha-Ko-8B-Evo
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

trust_remote_code: true
# is_mistral_derived_model: true
# trust_remote_code: true

load_in_8bit: false
load_in_4bit: true

datasets:
  - path: allganize/finance-multiple-choice-ko-240605-processed
    type: sharegpt
    conversation: llama3

dataset_prepared_path: ./data/prepared_dataset
val_set_size: 0.002
output_dir: ./data/models/llama3-alpha-ko-fmmlu-exp2
chat_template: llama3

sequence_len: 4096  # supports up to 8192
sample_packing: false
eval_sample_packing: false
pad_to_sequence_len: true

adapter: qlora
lora_model_dir:
lora_r: 256
lora_alpha: 128
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_target_modules:
  - gate_proj
  - down_proj
  - up_proj
  - q_proj
  - v_proj
  - k_proj
  - o_proj
lora_modules_to_save:
  - embed_tokens
  - lm_head

wandb_project: llama3
wandb_entity:
wandb_watch:
wandb_name: llama3-alpha-ko-fmmlu-exp2
wandb_log_model:

max_grad_norm: 1.0
gradient_accumulation_steps: 16
micro_batch_size: 4
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: constant
learning_rate: 1e-5

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: true

loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3

debug: true

warmup_steps: 100
eval_steps: 0.10
eval_table_size:
eval_table_max_new_tokens: 128
save_steps: 200 # 전체 스텝이 13725라 0.1 같이 퍼센트로 바꿔도 될듯.
save_total_limit: 4
weight_decay: 0.01
deepspeed:
fsdp:
fsdp_config:
special_tokens:
  eos_token: "<|eot_id|>"
  pad_token: "<|eot_id|>"

```

</details><br>

# data/models/llama3-alpha-ko-fmmlu-exp2

This model is a fine-tuned version of [allganize/Llama-3-Alpha-Ko-8B-Evo](https://huggingface.co/allganize/Llama-3-Alpha-Ko-8B-Evo) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3217

## 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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 100
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.0954        | 0.0104 | 1    | 1.0094          |
| 0.7671        | 0.1036 | 10   | 0.7111          |
| 0.6585        | 0.2071 | 20   | 0.6337          |
| 0.6094        | 0.3107 | 30   | 0.5744          |
| 0.518         | 0.4142 | 40   | 0.5192          |
| 0.5139        | 0.5178 | 50   | 0.4666          |
| 0.3988        | 0.6214 | 60   | 0.4250          |
| 0.4307        | 0.7249 | 70   | 0.3842          |
| 0.4507        | 0.8285 | 80   | 0.3551          |
| 0.4253        | 0.9320 | 90   | 0.3217          |


### Framework versions

- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.1.2+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1