---
license: apache-2.0
base_model: teknium/OpenHermes-2.5-Mistral-7B
tags:
- generated_from_trainer
model-index:
- name: Eileithyia-7B
  results: []
---

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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 [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) on a private dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4546

## Model description

Eileithyia-7B is an unaligned, roleplay oriented model created by merging teknium/OpenHermes-2.5-Mistral-7B with a bespoke LORA trained directly on OpenHermes.

Eileithyia, as is the current trend, is named after a Greek goddess; in this case it is the goddess of childbirth and pregnancy.

## Training and evaluation data

The private ~400k token dataset used to train the LORA was Alpaca formatted and focused on 4 primary categories:

    - Medical texts (on pregnancy, reproductive organs, and impregnation). These are formatted so the model, in character as a doctor, answers a patient's question in short to medium form.
    - Excerpts from short stories and novellas (erotic, romantic, and platonic) centered around both realistic and fantastic pregnancy. 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 related terms then human curated (...the things I’ve seen…). These are converted to Alpaca with “Enter RP mode.” in all the instruction fields.
    - ~42k tokens of GPT-4 generated data on pregnancy from various characters’ perspectives, focusing on different responses and stages. Also includes a synopsis for each week in various styles.
    - ~18k tokens of GPT-4 generated data on non-maternal 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 5
- total_train_batch_size: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5629        | 0.75  | 25   | 1.6511          |
| 1.5253        | 1.5   | 50   | 1.5730          |
| 1.3363        | 2.25  | 75   | 1.5014          |
| 1.4017        | 2.99  | 100  | 1.4690          |
| 1.2677        | 3.74  | 125  | 1.4593          |
| 1.351         | 4.49  | 150  | 1.4546          |


### Framework versions

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