Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets
  • Docs
  • Enterprise
  • Pricing
    • Website
      • Tasks
      • HuggingChat
      • Collections
      • Languages
      • Organizations
    • Community
      • Blog
      • Posts
      • Daily Papers
      • Hardware
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

HelloSun
/
flint_cpu_opt

Text Generation
GGUF
llama.cpp
cpu
avx512-vnni
amx
Mixture of Experts
flint
nvfp4
conversational
Model card Files Files and versions
xet
Community

Instructions to use HelloSun/flint_cpu_opt 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 HelloSun/flint_cpu_opt 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 HelloSun/flint_cpu_opt:UD-Q4_K_XL
    # Run inference directly in the terminal:
    llama cli -hf HelloSun/flint_cpu_opt:UD-Q4_K_XL
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama serve -hf HelloSun/flint_cpu_opt:UD-Q4_K_XL
    # Run inference directly in the terminal:
    llama cli -hf HelloSun/flint_cpu_opt:UD-Q4_K_XL
    Build from source code
    git clone https://github.com/ggml-org/llama.cpp.git
    cd llama.cpp
    cmake -B build
    cmake --build build -j --target llama-app
    # Start a local OpenAI-compatible server with a web UI:
    ./build/bin/llama serve -hf HelloSun/flint_cpu_opt:UD-Q4_K_XL
    # Run inference directly in the terminal:
    ./build/bin/llama cli -hf HelloSun/flint_cpu_opt:UD-Q4_K_XL
    Use Docker
    docker model run hf.co/HelloSun/flint_cpu_opt:UD-Q4_K_XL
  • Llama new
  • LM Studio
  • Jan
  • vLLM

    How to use HelloSun/flint_cpu_opt with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "HelloSun/flint_cpu_opt"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "HelloSun/flint_cpu_opt",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/HelloSun/flint_cpu_opt:UD-Q4_K_XL
  • Ollama

    How to use HelloSun/flint_cpu_opt with Ollama:

    ollama run hf.co/HelloSun/flint_cpu_opt:UD-Q4_K_XL
  • Unsloth Desktop
  • Pi

    How to use HelloSun/flint_cpu_opt with Pi:

    Start the llama.cpp server
    # Install llama.cpp:
    brew install llama.cpp
    # Start a local OpenAI-compatible server:
    llama serve -hf HelloSun/flint_cpu_opt:UD-Q4_K_XL
    Configure the model in Pi
    # Install Pi:
    npm install -g @earendil-works/pi-coding-agent
    # Add to ~/.pi/agent/models.json:
    {
      "providers": {
        "llama-cpp": {
          "baseUrl": "http://localhost:8080/v1",
          "api": "openai-completions",
          "apiKey": "none",
          "models": [
            {
              "id": "HelloSun/flint_cpu_opt:UD-Q4_K_XL"
            }
          ]
        }
      }
    }
    Run Pi
    # Start Pi in your project directory:
    pi
  • Docker Model Runner

    How to use HelloSun/flint_cpu_opt with Docker Model Runner:

    docker model run hf.co/HelloSun/flint_cpu_opt:UD-Q4_K_XL
  • Lemonade

    How to use HelloSun/flint_cpu_opt with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull HelloSun/flint_cpu_opt:UD-Q4_K_XL
    Run and chat with the model
    lemonade run user.flint_cpu_opt-UD-Q4_K_XL
    List all available models
    lemonade list
  • Hermes Agent

    How to use HelloSun/flint_cpu_opt with Hermes Agent:

    Start the llama.cpp server
    # Install llama.cpp:
    brew install llama.cpp
    # Start a local OpenAI-compatible server:
    llama serve -hf HelloSun/flint_cpu_opt:UD-Q4_K_XL
    Configure Hermes
    # 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 HelloSun/flint_cpu_opt:UD-Q4_K_XL
    Run Hermes
    hermes
  • Atomic Chat
  • OpenClaw

    How to use HelloSun/flint_cpu_opt with OpenClaw:

    Start the llama.cpp server
    # Install llama.cpp:
    brew install llama.cpp
    # Start a local OpenAI-compatible server:
    llama serve -hf HelloSun/flint_cpu_opt:UD-Q4_K_XL
    Configure OpenClaw
    # Install OpenClaw:
    npm install -g openclaw@latest
    # Register the local server and set it as the default model:
    openclaw onboard --non-interactive --mode local \
      --auth-choice custom-api-key \
      --custom-base-url http://127.0.0.1:8080/v1 \
      --custom-model-id "HelloSun/flint_cpu_opt:UD-Q4_K_XL" \
      --custom-provider-id llama-cpp \
      --custom-compatibility openai \
      --custom-text-input \
      --accept-risk \
      --skip-health
    Run OpenClaw
    openclaw agent --local --agent main --message "Hello from Hugging Face"
flint_cpu_opt / patches
99.7 kB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 5 commits
HelloSun's picture
HelloSun
Add NVFP4 CPU kernels results, integrate model card, add handoff
45f1d0b verified about 3 hours ago
  • BASE_COMMIT
    41 Bytes
    flint CPU opt: IQ4_XS AVX512-VNNI vec_dot, Q5_K mask merge, rebased on de5122d; re-measured benchmarks and KLD 6 days ago
  • flint-cpu-opt.patch
    96.2 kB
    Add NVFP4 CPU kernels results, integrate model card, add handoff about 3 hours ago
  • optional-profiler.patch
    3.48 kB
    flint CPU opt: IQ4_XS AVX512-VNNI vec_dot, Q5_K mask merge, rebased on de5122d; re-measured benchmarks and KLD 6 days ago