Instructions to use younissk/nanoBeard-sloop-14M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use younissk/nanoBeard-sloop-14M-GGUF 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 younissk/nanoBeard-sloop-14M-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf younissk/nanoBeard-sloop-14M-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf younissk/nanoBeard-sloop-14M-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf younissk/nanoBeard-sloop-14M-GGUF: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 younissk/nanoBeard-sloop-14M-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf younissk/nanoBeard-sloop-14M-GGUF: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 younissk/nanoBeard-sloop-14M-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf younissk/nanoBeard-sloop-14M-GGUF:Q4_K_M
Use Docker
docker model run hf.co/younissk/nanoBeard-sloop-14M-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use younissk/nanoBeard-sloop-14M-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "younissk/nanoBeard-sloop-14M-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "younissk/nanoBeard-sloop-14M-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/younissk/nanoBeard-sloop-14M-GGUF:Q4_K_M
- Ollama
How to use younissk/nanoBeard-sloop-14M-GGUF with Ollama:
ollama run hf.co/younissk/nanoBeard-sloop-14M-GGUF:Q4_K_M
- Unsloth Studio
How to use younissk/nanoBeard-sloop-14M-GGUF 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 younissk/nanoBeard-sloop-14M-GGUF 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 younissk/nanoBeard-sloop-14M-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for younissk/nanoBeard-sloop-14M-GGUF to start chatting
- Docker Model Runner
How to use younissk/nanoBeard-sloop-14M-GGUF with Docker Model Runner:
docker model run hf.co/younissk/nanoBeard-sloop-14M-GGUF:Q4_K_M
- Lemonade
How to use younissk/nanoBeard-sloop-14M-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull younissk/nanoBeard-sloop-14M-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.nanoBeard-sloop-14M-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
nanoBeard Sloop 14M (pirate, GPT-2)
GGUF quantizations of nanoBeard Sloop โ a tiny 14M GPT-2-style pirate text model (completion, no chat turns), loaded via llama.cpp's gpt2 arch (14M params), for on-device inference with
llama.cpp and the NanoBeard mobile app.
Val loss โ 4.282.
Files
| file | quant | size | use |
|---|---|---|---|
sloop-14M.Q4_K_M.gguf |
Q4_K_M | 11.1 MB | default โ phones |
sloop-14M.Q8_0.gguf |
Q8_0 | 14.7 MB | quality fallback |
Q4_K_M is the default for phones (smallest + fastest). Q8_0 is a near-lossless
fallback when you have the storage and want max quality.
Chat format
Plain-text turns (no chat template). Build the prompt as:
User: <your message>
Pirate:
Turns are separated by a single newline; stop generation at the <|endoftext|>
token. Example with llama.cpp:
llama-completion -m sloop-14M.Q4_K_M.gguf \
-p $'User: Tell me about the sea.\nPirate:' -n 80 --temp 0.8 --top-k 40
Tokenizer
Custom 16,384-token byte-level BPE (GPT-2-style pre-tokenizer), embedded in the GGUF. No external tokenizer file required.
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