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alac/Waxwing-Storytelling-70B-LoRA
Waxwing-Storytelling-70B-LoRA is a text generation model from alac. Use it when you need the model to write or continue text. The card lists the license as other.
Waxwing is a storytelling lora for Llama 2 70B. - Guide the story with Waxwing's turn-based instruction system. - Tailor the feel of your story using style tags. - Experience storytelling free of ChatGPT's idiosyncras…
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From the Hugging Face model README
Waxwing is a storytelling lora for Llama 2 70B.
Waxwing is available:
By using this model, you take full responsibility for anything done with its outputs.
Waxwing was trained with a small dataset gathered from public domain writing. The exact dataset will remain private, but the code used to generate prompts and metadata is available on github. Upstage's SOLAR model was used to tag the dataset.
### System:
A chat between a user and a writing assistant.
{context}
### User:
{style tags}
Write a scene where: {events that should happen in the next scene}
### Assistant:
{output}
context is an optional story synopsis.
style tags should be a string along the lines of:
Tone: {list of tones}. Writing style: {list of writing styles}.
Written with {slow|medium|fast} pacing, in moment to moment detail, in {abstract|selective|vivid sensory} detail, from a {First|Third Person (Character)} perspective.
The exact values it was trained on are in the dataset_tags.json file. Anecdotally, it works better with a subset of the style tags used (Tone: tense) or with tags that are complementary (Tone: tense, mysterious. Writing style: dramatic. Written in abstract detail.). It's unclear how well Waxwing responds to tags that it was not trained on (e.g. 'genre').
For SillyTavern users, the style tags work well in the "Author's Note" field at depth 1. User messages should begin with Write a scene where: ; to continue a scene, just type continue. Most testing was done using the Genesis preset.
Waxwing was trained on a single machine with 72GB of VRAM. The training parameters are available in the training_parameters.json file of the main branch. The software used to train was FartyPants' Training_PRO extension for the Oobabooga Text Generation WebUI.