---
license: cc-by-nc-4.0
base_model: NeverSleep/Nethena-20B
tags:
- generated_from_trainer
model-index:
- name: lora-outA
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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[<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)

This model is a fine-tuned version of [NeverSleep/Nethena-20B](https://huggingface.co/NeverSleep/Nethena-20B) on a private dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3864

## Model description

athirdpath/Nethena-20b-Glued-LORA is a 128 rank LORA for RP, trained on [NeverSleep/Nethena-20B](https://huggingface.co/NeverSleep/Nethena-20B). It is unalligned and NSFW-oriented.

This is a test, exploring the effects of "gluing" the components of the 20b model together to reduce the iconic word replacement errors, increase lucidity, and improve recall.

## Training and evaluation data

The private ~500k token dataset used to train the LORA was Alpaca formatted and focused on 4 primary categories:

    - Medical texts (on psychology, reproductive organs, anatomy, and pregnancy). These are formatted so the model, in character as a doctor or therapist, answers a patient's question in short to medium form.
    - Excerpts from short stories and novellas (erotic and romantic) centered around both realistic and fantastic situations, covering several fetishes as well. These are sliced into ~2048 token chunks, and these long-form responses are all tied to the command “Enter narrator mode.” in the instructions.
    - A selection from PIPPA, using a wide keyword search for tokens associated with low quality human or AI data to remove those responses, then a positive search was done for words and phrases associated with a higher reading level. These are converted to Alpaca with “Enter RP mode.” in all the instruction fields.
    - ~18k tokens of GPT-4 generated data on role-playing from various characters’ perspectives, focusing on different situations and emotions. Includes many multi-turn conversations.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 5
- total_train_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.955         | 0.38  | 25   | 1.9037          |
| 1.6598        | 0.75  | 50   | 1.6192          |
| 1.5649        | 1.13  | 75   | 1.5010          |
| 1.4424        | 1.5   | 100  | 1.4424          |
| 1.4142        | 1.88  | 125  | 1.4068          |
| 1.4951        | 2.25  | 150  | 1.3908          |
| 1.4418        | 2.63  | 175  | 1.3864          |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1