---
language:
- en
license: mit
tags:
- generated_from_trainer
- text generation
- pytorch
- casual-lm
metrics:
- accuracy
base_model: EleutherAI/gpt-neo-125M
model-index:
- name: openchatgpt-neo-r1
  results: []
---

# --- Disclaimer ---

# "Neo is an incredibly cursed codebase, it should not be used by anyone" (C) co-founder of EleutherAI - Connor Leahy

# !!! USE [openchatgpt-neox-125m](https://huggingface.co/mrsteyk/openchatgpt-neox-125m) INSTEAD !!!

# --- Archived ---

# openchatgpt-neo-r1

This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the openchatgpt safe-r1 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2156
- Accuracy: 0.8338

## Model description

Finetune based on the inner workings of ChatGPT. I won't elaborate on that. You must have a faint idea of how prompt is made for it to spit anything that's not garbled mess.

This is effectively a schizophrenic idea that met the light of day. Practically a collab of 3 students in a virtual shed.

## Intended uses & limitations

Intended uses & limitations fall in line with OpenAI's. Dataset used consists of safe texts (i.e. not highly sexual/erotica type stuff). NSFW version of the dataset is not planned to exist at the moment.

Keep in mind that this is a 125m version of GPT-Neo. My 1050Ti Mobile couldn't even handle that without gradient thingmabobs. If anyone knows how to effectively finetune larger models on free colabs - feel free to let me know. Pile tokenizer also has one downside compared to native GPT-2/3 - `Assistant`.

## Training and evaluation data

Data was split in ratio of 95%/5%. Preproccess included removing mentions of OpenAI wherever it was not deemed appropriete (GPT-2 has one of the appropriete mentions). Whole dataset consists of just shy off 3k input-output pairs. One input has multiple outputs (read as: one message has multiple variants of an answer). <<<1% (3 total) are curated lines (i.e. a huge mistake was spotted that needed corrections).

Heavy bias on IT.

## Training procedure

Input and output were straight up concatenated due to the nature of how ChatGPT works. Padding chosen was the same as the separator token, if that's not effective - please let me know as I am new to this stuff.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.9203        | 1.0   | 1378 | 5.1668          | 0.7274   |
| 4.1368        | 2.0   | 2756 | 4.3841          | 0.7563   |
| 3.4554        | 3.0   | 4134 | 3.8068          | 0.7875   |
| 2.7598        | 4.0   | 5512 | 3.3097          | 0.8303   |
| 2.5879        | 5.0   | 6890 | 3.2156          | 0.8338   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2