TFMC/imatrix-dataset-for-japanese-llm
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How to use mmnga/Light-R1-32B-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="mmnga/Light-R1-32B-gguf", filename="Light-R1-32B-IQ1_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
How to use mmnga/Light-R1-32B-gguf with llama.cpp:
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf mmnga/Light-R1-32B-gguf:Q4_K_M # Run inference directly in the terminal: llama-cli -hf mmnga/Light-R1-32B-gguf:Q4_K_M
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf mmnga/Light-R1-32B-gguf:Q4_K_M # Run inference directly in the terminal: llama-cli -hf mmnga/Light-R1-32B-gguf:Q4_K_M
# 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 mmnga/Light-R1-32B-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf mmnga/Light-R1-32B-gguf:Q4_K_M
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 mmnga/Light-R1-32B-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf mmnga/Light-R1-32B-gguf:Q4_K_M
docker model run hf.co/mmnga/Light-R1-32B-gguf:Q4_K_M
How to use mmnga/Light-R1-32B-gguf with Ollama:
ollama run hf.co/mmnga/Light-R1-32B-gguf:Q4_K_M
How to use mmnga/Light-R1-32B-gguf with Unsloth Studio:
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 mmnga/Light-R1-32B-gguf to start chatting
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 mmnga/Light-R1-32B-gguf to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for mmnga/Light-R1-32B-gguf to start chatting
How to use mmnga/Light-R1-32B-gguf with Pi:
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf mmnga/Light-R1-32B-gguf:Q4_K_M
# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
"providers": {
"llama-cpp": {
"baseUrl": "http://localhost:8080/v1",
"api": "openai-completions",
"apiKey": "none",
"models": [
{
"id": "mmnga/Light-R1-32B-gguf:Q4_K_M"
}
]
}
}
}# Start Pi in your project directory: pi
How to use mmnga/Light-R1-32B-gguf with Hermes Agent:
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf mmnga/Light-R1-32B-gguf:Q4_K_M
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default mmnga/Light-R1-32B-gguf:Q4_K_M
hermes
How to use mmnga/Light-R1-32B-gguf with Docker Model Runner:
docker model run hf.co/mmnga/Light-R1-32B-gguf:Q4_K_M
How to use mmnga/Light-R1-32B-gguf with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mmnga/Light-R1-32B-gguf:Q4_K_M
lemonade run user.Light-R1-32B-gguf-Q4_K_M
lemonade list
qihoo360さんが公開しているLight-R1-32Bのggufフォーマット変換版です。
imatrixのデータはTFMC/imatrix-dataset-for-japanese-llmを使用して作成しました。
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build -DGGML_CUDA=ON
cmake --build build --config Release
build/bin/llama-cli -m 'Light-R1-32B-gguf' -n 128 -c 128 -p 'あなたはプロの料理人です。レシピを教えて' -cnv
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docker model run hf.co/mmnga/Light-R1-32B-gguf: