- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-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 featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-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 featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-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 featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-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 featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF:Q4_K_M
Use Docker
docker model run hf.co/featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'Use Docker
docker model run hf.co/featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF:Q4_K_M
- Ollama
How to use featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF with Ollama:
ollama run hf.co/featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF with Docker Model Runner:
docker model run hf.co/featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF:Q4_K_M
- Lemonade
How to use featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/
lemonade pull featherless-ai-quants/AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.AMindToThink-gemma-2-2b-it_RMU_s200_a500_layer11-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
