Instructions to use theprint/tinyllama_alpaca_cthulhu_small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use theprint/tinyllama_alpaca_cthulhu_small with Transformers:

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

pipe = pipeline("text-generation", model="theprint/tinyllama_alpaca_cthulhu_small", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("theprint/tinyllama_alpaca_cthulhu_small", dtype="auto", device_map="auto")

llama-cpp-python

How to use theprint/tinyllama_alpaca_cthulhu_small with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="theprint/tinyllama_alpaca_cthulhu_small",
	filename="tinyllama_alpaca_cthulhu_small-unsloth.F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use theprint/tinyllama_alpaca_cthulhu_small with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf theprint/tinyllama_alpaca_cthulhu_small:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf theprint/tinyllama_alpaca_cthulhu_small:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf theprint/tinyllama_alpaca_cthulhu_small:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf theprint/tinyllama_alpaca_cthulhu_small:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf theprint/tinyllama_alpaca_cthulhu_small:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf theprint/tinyllama_alpaca_cthulhu_small:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf theprint/tinyllama_alpaca_cthulhu_small:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf theprint/tinyllama_alpaca_cthulhu_small:Q4_K_M

Use Docker

docker model run hf.co/theprint/tinyllama_alpaca_cthulhu_small:Q4_K_M

LM Studio
Jan

vLLM

How to use theprint/tinyllama_alpaca_cthulhu_small with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/theprint/tinyllama_alpaca_cthulhu_small:Q4_K_M

SGLang

How to use theprint/tinyllama_alpaca_cthulhu_small 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 "theprint/tinyllama_alpaca_cthulhu_small" \
    --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": "theprint/tinyllama_alpaca_cthulhu_small",
		"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 "theprint/tinyllama_alpaca_cthulhu_small" \
        --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": "theprint/tinyllama_alpaca_cthulhu_small",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use theprint/tinyllama_alpaca_cthulhu_small with Ollama:
```
ollama run hf.co/theprint/tinyllama_alpaca_cthulhu_small:Q4_K_M
```

Unsloth Studio

How to use theprint/tinyllama_alpaca_cthulhu_small with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for theprint/tinyllama_alpaca_cthulhu_small to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for theprint/tinyllama_alpaca_cthulhu_small to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for theprint/tinyllama_alpaca_cthulhu_small to start chatting

Atomic Chat new
Docker Model Runner
How to use theprint/tinyllama_alpaca_cthulhu_small with Docker Model Runner:
```
docker model run hf.co/theprint/tinyllama_alpaca_cthulhu_small:Q4_K_M
```

Lemonade

How to use theprint/tinyllama_alpaca_cthulhu_small with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull theprint/tinyllama_alpaca_cthulhu_small:Q4_K_M

Run and chat with the model

lemonade run user.tinyllama_alpaca_cthulhu_small-Q4_K_M

List all available models

lemonade list

IA IA! A tiny Cthulhu cultist! This TinyLlama variant is fine tuned on Cthulhu Mythos, so you can have your very own cultist AI friend.

5/3/24 Update: The model was given a bit more training and several gguf files were uploaded.

This model was mainly created to test a cthulhu-fied data set. This tiny model is a proof of concept, before a larger model is trained on the full data set. At that point, I will also make the data set public.

The Cthulhu Mythos data set is based on alpaca-cleaned, except all the replies have been re-written to sound like they were given by a cultist of Cthulhu. Only a subset of the data (10k entries) was used to train the first iteration of this model

Uploaded model

Developed by: theprint
License: apache-2.0
Finetuned from model : unsloth/tinyllama-bnb-4bit

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: 124

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

6-bit

8-bit

16-bit

Model tree for theprint/tinyllama_alpaca_cthulhu_small

Base model

unsloth/tinyllama-bnb-4bit

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Collection including theprint/tinyllama_alpaca_cthulhu_small

The Cthulhu Mythos Collection

Collection

4 items • Updated Sep 16, 2025 • 1