Instructions to use schangg/sprite-100m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use schangg/sprite-100m 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 schangg/sprite-100m:Q8_0
# Run inference directly in the terminal:
llama cli -hf schangg/sprite-100m:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf schangg/sprite-100m:Q8_0
# Run inference directly in the terminal:
llama cli -hf schangg/sprite-100m:Q8_0

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 schangg/sprite-100m:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf schangg/sprite-100m:Q8_0

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 schangg/sprite-100m:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf schangg/sprite-100m:Q8_0

Use Docker

docker model run hf.co/schangg/sprite-100m:Q8_0

LM Studio
Jan

vLLM

How to use schangg/sprite-100m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "schangg/sprite-100m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "schangg/sprite-100m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/schangg/sprite-100m:Q8_0

Ollama
How to use schangg/sprite-100m with Ollama:
```
ollama run hf.co/schangg/sprite-100m:Q8_0
```

Unsloth Studio

How to use schangg/sprite-100m 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 schangg/sprite-100m 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 schangg/sprite-100m to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for schangg/sprite-100m to start chatting

Docker Model Runner
How to use schangg/sprite-100m with Docker Model Runner:
```
docker model run hf.co/schangg/sprite-100m:Q8_0
```

Lemonade

How to use schangg/sprite-100m with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull schangg/sprite-100m:Q8_0

Run and chat with the model

lemonade run user.sprite-100m-Q8_0

List all available models

lemonade list

Atomic Chat

New discussion

Resources

View closed (0)

Welcome to the community

The community tab is the place to discuss and collaborate with the HF community!