---
base_model: EleutherAI/pythia-125m-deduped
library_name: peft
license: apache-2.0
tags:
- axolotl
- generated_from_trainer
model-index:
- name: pythia-125m-gpt4-llm-cleaned
  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: EleutherAI/pythia-125m-deduped
load_in_8bit: false
datasets:
  - path: jtatman/storywriting_combined_instruct
    type: alpaca
dataset_prepared_path: ds-storytelling
val_set_size: 0.05
adapter: lora
lora_model_dir:
sequence_len: 512
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
lora_target_modules:
  - query_key_value
lora_target_linear: true 
lora_fan_in_fan_out: true  # pythia/GPTNeoX lora specific
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
output_dir: ./outputs/lora-alpaca-pythia-125m
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 4
learning_rate: 0.00001
train_on_inputs: false
group_by_length: false
bf16: auto
tf32: false
float16: true
gpu_memory_limit: 8GiB
hub_model_id: jtatman/pythia-125m-gpt4-llm-cleaned 
lora_on_cpu: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
weight_decay: 0.1
evals_per_epoch: 4
logging_steps: 1

```

</details><br>

# pythia-125m-gpt4-llm-cleaned

This model is a fine-tuned version of [EleutherAI/pythia-125m-deduped](https://huggingface.co/EleutherAI/pythia-125m-deduped) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6093

## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 4

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.1847        | 0.0008 | 1    | 2.7080          |
| 2.8391        | 0.2504 | 326  | 2.6464          |
| 2.9722        | 0.5008 | 652  | 2.6312          |
| 2.9904        | 0.7512 | 978  | 2.6245          |
| 3.1704        | 1.0015 | 1304 | 2.6179          |
| 3.1108        | 1.2519 | 1630 | 2.6155          |
| 2.9321        | 1.5023 | 1956 | 2.6143          |
| 3.0499        | 1.7527 | 2282 | 2.6113          |
| 2.9776        | 2.0031 | 2608 | 2.6102          |
| 2.7773        | 2.2535 | 2934 | 2.6102          |
| 3.5383        | 2.5038 | 3260 | 2.6106          |
| 2.8105        | 2.7542 | 3586 | 2.6106          |
| 3.0778        | 3.0046 | 3912 | 2.6115          |
| 2.9706        | 3.2550 | 4238 | 2.6096          |
| 2.7671        | 3.5054 | 4564 | 2.6100          |
| 3.3049        | 3.7558 | 4890 | 2.6093          |


### Framework versions

- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1