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---
license: mit
language:
- en
tags:
- generated_from_trainer
- text generation
- pytorch
- casual-lm
metrics:
- accuracy
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
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