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"
build.sh: 設定 pi 預設 provider/model 使用本地 llama-server
Browse files
build.sh
CHANGED
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@@ -127,6 +127,32 @@ with open(path, "w") as f:
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print(f"[build.sh] 已寫入 {path}")
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WRAPPER="$HOME/.local/bin/pi-local"
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cat > "$WRAPPER" <<EOS
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#!/usr/bin/env bash
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print(f"[build.sh] 已寫入 {path}")
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PY
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# 讓 pi 預設就用這個 llama-server(不只知道 provider,還要設成啟動預設)
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PI_SETTINGS_JSON="$HOME/.pi/agent/settings.json"
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info "設定 pi 預設模型:$PI_PROVIDER_ID/$PI_MODEL_ID"
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python3 - "$PI_SETTINGS_JSON" "$PI_PROVIDER_ID" "$PI_MODEL_ID" <<'PY'
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import json, os, sys
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path, provider, model = sys.argv[1:4]
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os.makedirs(os.path.dirname(path), exist_ok=True)
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settings = {}
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if os.path.exists(path):
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try:
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with open(path) as f:
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loaded = json.load(f)
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if isinstance(loaded, dict):
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settings = loaded
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except Exception as exc:
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print(f"[WARN] 無法解析舊 settings.json,將覆蓋:{exc}")
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settings["defaultProvider"] = provider
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settings["defaultModel"] = model
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enabled = settings.get("enabledModels")
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if not isinstance(enabled, list) or not enabled:
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settings["enabledModels"] = [f"{provider}/{model}"]
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with open(path, "w") as f:
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json.dump(settings, f, indent=2)
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print(f"[build.sh] 已寫入 {path}")
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WRAPPER="$HOME/.local/bin/pi-local"
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cat > "$WRAPPER" <<EOS
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#!/usr/bin/env bash
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