Instructions to use Dans-Archive/Dans-CreepingSenseOfDoom-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dans-Archive/Dans-CreepingSenseOfDoom-13b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Dans-Archive/Dans-CreepingSenseOfDoom-13b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Dans-Archive/Dans-CreepingSenseOfDoom-13b") model = AutoModelForCausalLM.from_pretrained("Dans-Archive/Dans-CreepingSenseOfDoom-13b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Dans-Archive/Dans-CreepingSenseOfDoom-13b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Dans-Archive/Dans-CreepingSenseOfDoom-13b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Dans-Archive/Dans-CreepingSenseOfDoom-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Dans-Archive/Dans-CreepingSenseOfDoom-13b
- SGLang
How to use Dans-Archive/Dans-CreepingSenseOfDoom-13b 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 "Dans-Archive/Dans-CreepingSenseOfDoom-13b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Dans-Archive/Dans-CreepingSenseOfDoom-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "Dans-Archive/Dans-CreepingSenseOfDoom-13b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Dans-Archive/Dans-CreepingSenseOfDoom-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Dans-Archive/Dans-CreepingSenseOfDoom-13b with Docker Model Runner:
docker model run hf.co/Dans-Archive/Dans-CreepingSenseOfDoom-13b
What is the model for?
This model is proficient in crafting text-based adventure games. It can both concise replies and more expansive, novel-like descriptions. The ability to alternate between these two response styles can be triggered by a distinct system message.
What's in the sausage?
This model was trained on Holodeck-1 using a deduped version of the skein text adventure dataset augmented with system messages using the 'Metharme' prompting format.
PROMPT FORMAT:
Consistent with the Pygmalion Metharme format which is shown below.
<|system|>{system message here}<|user|>{user action here}<|model|>{model response}
<|system|>{system message here}<|model|>{model response}
<|system|>{system message here}<|user|>{user action here}<|model|>{model response}<|user|>{user action here}<|model|>{model response}
EXAMPLES:
For shorter responses:
<|system|>Mode: Adventure
Theme: Science Fiction, cats, money, aliens, space, stars, siblings, future, trade
Tense: Second person present
Extra: Short response length<|user|>you look around<|model|>{CURSOR HERE}
<|system|>You are a dungeon master of sorts, guiding the reader through a story based on the following themes: Lovecraftian, Horror, city, research. Do not be afraid to get creative with your responses or to tell them they can't do something when it doesnt make sense for the situation. Narrate their actions and observations as they occur and drive the story forward.<|user|>you look around<|model|>{CURSOR HERE}
For longer novel like responses:
<|system|>You're tasked with creating an interactive story around the genres of historical, historical, RPG, serious. Guide the user through this tale, describing their actions and surroundings using second person present tense. Lengthy and descriptive responses will enhance the experience.<|user|>you look around<|model|>{CURSOR HERE}
With a model message first:
<|system|>Mode: Story
Theme: fantasy, female protagonist, grimdark
Perspective and Tense: Second person present
Directions: Write something to hook the user into the story then narrate their actions and observations as they occur while driving the story forward.<|model|>{CURSOR HERE}
Some quick and dirty training details:

- Sequence length: 4096
- # of epochs: 3
- Training time: 8 hours
- Hardware: 1x RTX 3090
- Training type: QLoRA
- PEFT R/A: 32/32
Credits:
Holodeck-1:
Thank you to Mr. Seeker and the Kobold AI team for the wonderful model Holodeck-1
Skein Text Adventure Data:
Thank you to the Kobold AI community for curating the Skein dataset, which is pivotal to this model's capabilities.
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