tags:
- autotrain
- text-generation
- text-generation-inference
- peft
- lora
library_name: transformers
base_model: google/gemma-2-2b-it
datasets:
- TristanBehrens/lovecraftcorpus
license: other
widget:
- text: Write a short horror passage in the style of H. P. Lovecraft.
🐙 theoracle/hplovecraft Gemma-2B-IT finetuned on Lovecraft’s cosmic-horror corpus Overview
theoracle/hplovecraft is a LoRA-finetuned version of google/gemma-2-2b-it, trained on the TristanBehrens/lovecraftcorpus dataset using AutoTrain Advanced.
The objective of this model is to reproduce the literary tone and thematic patterns typical of H. P. Lovecraft, including:
dense atmospheric descriptions
archaic vocabulary and formal cadence
cosmic dread and metaphysical terror
first-person “confessional” narration
references to forbidden knowledge, ancient cults, and non-Euclidean horrors
This model is intended for creative writing, fiction generation, and experimentation with stylistic conditioning.
Usage
Minimal working example:
from transformers import pipeline
pipe = pipeline( "text-generation", model="theoracle/hplovecraft", max_new_tokens=300, temperature=0.9, top_p=0.9, )
prompt = "At dusk, I heard the distant cry of something not meant for human ears..." print(pipe(prompt)[0]["generated_text"])
Training Details
Base model: google/gemma-2-2b-it
Method: LoRA (PEFT)
Trainer: AutoTrain Advanced
Dataset: TristanBehrens/lovecraftcorpus
Task: Supervised fine-tuning for causal LM
Block size: 1024
Epochs: 2
Precision: FP16
Quantization: INT4 during training (bitsandbytes)
Strengths
Strong stylistic fidelity to Lovecraft’s prose
Produces long, immersive horror passages
Good at evoking dread, ancient mythos, and cosmic insignificance
Maintains archaic tone without collapsing into incoherence
Limitations
May generate dark or disturbing content (intended for horror writing)
Not tuned for factual or instructional tasks
May over-use specific Lovecraft tropes when prompted repeatedly
Acknowledgements
Google for the Gemma family
Tristan Behrens for the dataset
Hugging Face AutoTrain for the training framework