Ring 2.0
Collection
14 items • Updated • 21
How to use inclusionAI/Ring-flash-2.0-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="inclusionAI/Ring-flash-2.0-GGUF", filename="Ring-flash-2.0-Q2_K.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
How to use inclusionAI/Ring-flash-2.0-GGUF with llama.cpp:
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf inclusionAI/Ring-flash-2.0-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf inclusionAI/Ring-flash-2.0-GGUF:Q4_K_M
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf inclusionAI/Ring-flash-2.0-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf inclusionAI/Ring-flash-2.0-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 inclusionAI/Ring-flash-2.0-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf inclusionAI/Ring-flash-2.0-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 inclusionAI/Ring-flash-2.0-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf inclusionAI/Ring-flash-2.0-GGUF:Q4_K_M
docker model run hf.co/inclusionAI/Ring-flash-2.0-GGUF:Q4_K_M
How to use inclusionAI/Ring-flash-2.0-GGUF with Ollama:
ollama run hf.co/inclusionAI/Ring-flash-2.0-GGUF:Q4_K_M
How to use inclusionAI/Ring-flash-2.0-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 inclusionAI/Ring-flash-2.0-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 inclusionAI/Ring-flash-2.0-GGUF to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for inclusionAI/Ring-flash-2.0-GGUF to start chatting
How to use inclusionAI/Ring-flash-2.0-GGUF with Docker Model Runner:
docker model run hf.co/inclusionAI/Ring-flash-2.0-GGUF:Q4_K_M
How to use inclusionAI/Ring-flash-2.0-GGUF with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull inclusionAI/Ring-flash-2.0-GGUF:Q4_K_M
lemonade run user.Ring-flash-2.0-GGUF-Q4_K_M
lemonade list
Use https://github.com/im0qianqian/llama.cpp to quantize.
For model inference, please download our release package from this url https://github.com/im0qianqian/llama.cpp/releases .
# Use a local model file
llama-cli -m my_model.gguf
# Launch OpenAI-compatible API server
llama-server -m my_model.gguf
Let's look forward to the following PR being merged:
2-bit
4-bit
6-bit
8-bit
Base model
inclusionAI/Ling-flash-base-2.0