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

Notebooks
Google Colab
Kaggle
Local Apps Settings

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 Desktop
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

Atomic Chat

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: 7

GGUF

Model size

807k params

Architecture

gpt2

Hardware compatibility

32-bit