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| 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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| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
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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) |
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| 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 |
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| ## Model description |
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| 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. |
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| Eileithyia, as is the current trend, is named after a Greek goddess; in this case it is the goddess of childbirth and pregnancy. |
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| ## Training and evaluation data |
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| The private ~400k token dataset used to train the LORA was Alpaca formatted and focused on 4 primary categories: |
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| - 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. |
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| ### Training hyperparameters |
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| 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 |
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| ### Training results |
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| | 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 | |
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| ### Framework versions |
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| - Transformers 4.34.1 |
| - Pytorch 2.1.0+cu118 |
| - Datasets 2.14.6 |
| - Tokenizers 0.14.1 |
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