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---
license:
- creativeml-openrail-m
language:
- en
tags:
- generated_from_trainer
- text generation
- pytorch
- casual-lm
metrics:
- accuracy
model-index:
- name: openchatgpt-neox-r1
results: []
---
# openchatgpt-neox-r1
This model is a fine-tuned version of [EleutherAI/pythia-125m-deduped](https://huggingface.co/EleutherAI/pythia-125m-deduped) on the openchatgpt safe-r1 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3585
- Accuracy: 0.9169
## 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.
BTW, Pythia is so much better omg.
## 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-NeoX (Pythia). My 1050Ti Mobile couldn't even handle that without gradient thingmabobs, 8BitAdam was also used. 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` is not 1 token, but 2.
## 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). At least 3 lines (<<<1% of line count, but more of byte count) are broken.
Heavy bias on IT.
## Training procedure
Input and output were straight up concatenated due to the nature of how ChatGPT works.
This time dataset was batched into groups of 2048 tokens. Meaning i got 628/31 groups for training/eval. Maybe that's what made the difference. EOS was also being used after the final separator.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1311 | 1.0 | 1377 | 1.3116 | 0.9127 |
| 0.6691 | 2.0 | 2754 | 1.2978 | 0.9160 |
| 0.3463 | 3.0 | 4131 | 1.3585 | 0.9169 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
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