Instructions to use network-centrality-labs/hplovecraft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use network-centrality-labs/hplovecraft with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="network-centrality-labs/hplovecraft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("network-centrality-labs/hplovecraft", dtype="auto")

PEFT
How to use network-centrality-labs/hplovecraft with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use network-centrality-labs/hplovecraft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "network-centrality-labs/hplovecraft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "network-centrality-labs/hplovecraft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/network-centrality-labs/hplovecraft

SGLang

How to use network-centrality-labs/hplovecraft with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "network-centrality-labs/hplovecraft" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "network-centrality-labs/hplovecraft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "network-centrality-labs/hplovecraft" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "network-centrality-labs/hplovecraft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use network-centrality-labs/hplovecraft with Docker Model Runner:
```
docker model run hf.co/network-centrality-labs/hplovecraft
```

🐙 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

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for network-centrality-labs/hplovecraft

Base model

google/gemma-2-2b

Finetuned

google/gemma-2-2b-it

Adapter

(448)

this model

Duplicated from theoracle/hplovecraft

network-centrality-labs
/

hplovecraft

Model tree for network-centrality-labs/hplovecraft

Dataset used to train network-centrality-labs/hplovecraft