---
library_name: peft
base_model: EleutherAI/pythia-14m
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 8f97fcaa-a2e2-4f79-8ff1-0ca9a91cd08f
  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: `0.4.1`
```yaml
adapter: lora
base_model: EleutherAI/pythia-14m
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - naver-news-summarization-ko_train_data.json
  ds_type: json
  path: /workspace/input_data/naver-news-summarization-ko_train_data.json
  type:
    field_input: document
    field_instruction: title
    field_output: summary
    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: ncbateman/8f97fcaa-a2e2-4f79-8ff1-0ca9a91cd08f
hub_repo: ncbateman
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: true
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_steps: 1000
micro_batch_size: 2
mlflow_experiment_name: /tmp/naver-news-summarization-ko_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 100
save_strategy: steps
sequence_len: 4096
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
val_set_size: 0.05
wandb_entity: breakfasthut
wandb_mode: online
wandb_project: tuning-miner
wandb_run: miner
wandb_runid: 8f97fcaa-a2e2-4f79-8ff1-0ca9a91cd08f
warmup_steps: 50
weight_decay: 0.0
xformers_attention: null

```

</details><br>

# 8f97fcaa-a2e2-4f79-8ff1-0ca9a91cd08f

This model is a fine-tuned version of [EleutherAI/pythia-14m](https://huggingface.co/EleutherAI/pythia-14m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1464

## 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: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- training_steps: 811

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 56.0062       | 0.0012 | 1    | 35.9780         |
| 4.6308        | 0.2505 | 203  | 4.4064          |
| 4.2248        | 0.5009 | 406  | 4.2050          |
| 4.0789        | 0.7514 | 609  | 4.1464          |


### Framework versions

- PEFT 0.13.2
- Transformers 4.45.2
- Pytorch 2.4.1+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1