Instructions to use DemonKing1234/Casual-TextTiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DemonKing1234/Casual-TextTiny with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="DemonKing1234/Casual-TextTiny",
	filename="Casual-TextTiny-F32.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use DemonKing1234/Casual-TextTiny 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 DemonKing1234/Casual-TextTiny:F32
# Run inference directly in the terminal:
llama cli -hf DemonKing1234/Casual-TextTiny:F32

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf DemonKing1234/Casual-TextTiny:F32
# Run inference directly in the terminal:
llama cli -hf DemonKing1234/Casual-TextTiny:F32

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 DemonKing1234/Casual-TextTiny:F32
# Run inference directly in the terminal:
./llama-cli -hf DemonKing1234/Casual-TextTiny:F32

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 DemonKing1234/Casual-TextTiny:F32
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DemonKing1234/Casual-TextTiny:F32

Use Docker

docker model run hf.co/DemonKing1234/Casual-TextTiny:F32

LM Studio
Jan

vLLM

How to use DemonKing1234/Casual-TextTiny with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DemonKing1234/Casual-TextTiny"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DemonKing1234/Casual-TextTiny",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DemonKing1234/Casual-TextTiny:F32

Ollama
How to use DemonKing1234/Casual-TextTiny with Ollama:
```
ollama run hf.co/DemonKing1234/Casual-TextTiny:F32
```

Unsloth Studio

How to use DemonKing1234/Casual-TextTiny 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 DemonKing1234/Casual-TextTiny 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 DemonKing1234/Casual-TextTiny to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DemonKing1234/Casual-TextTiny to start chatting

Atomic Chat new
Docker Model Runner
How to use DemonKing1234/Casual-TextTiny with Docker Model Runner:
```
docker model run hf.co/DemonKing1234/Casual-TextTiny:F32
```

Lemonade

How to use DemonKing1234/Casual-TextTiny with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DemonKing1234/Casual-TextTiny:F32

Run and chat with the model

lemonade run user.Casual-TextTiny-F32

List all available models

lemonade list

Casual-TextTiny (800K Parameter GPT)

A custom, character-level Generative Pre-trained Transformer (GPT) language model built and trained entirely from scratch.

This model is designed for ultra-lightweight local deployment and learning experimentation. It is optimized to run on standard CPUs using a custom Numba-accelerated inference engine with full KV-caching.

Highlights

Parameter Count: 801,408 (~800k)
Model File Size: 3.2 MB in GGUF format (Casual-TextTiny-F32.gguf)
Trained For: Creative text/story generation and simple instruction-following.

Architecture

Layers: 4 Block layers
Attention Heads: 8 Heads
Embedding Dimensions: 128
Context Length: 16 characters

Prompt Format

To trigger instruction-following behaviors, wrap your prompt in this template:

Downloads last month: 44

GGUF

Model size

807k params

Architecture

gpt2

Hardware compatibility

32-bit