readme: ---
  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
  