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"
Download benchmarks/kld_vs_stock.log from HelloSun/flint_cpu_opt: direct link, hf CLI and curl.
- Browser
- Download file 1.03 kB
-
https://huggingface.co/HelloSun/flint_cpu_opt/resolve/main/benchmarks/kld_vs_stock.log
- Command line
-
hf download hf://HelloSun/flint_cpu_opt/benchmarks/kld_vs_stock.log
-
curl -L -o kld_vs_stock.log https://huggingface.co/HelloSun/flint_cpu_opt/resolve/main/benchmarks/kld_vs_stock.log
1.03 kB
| == base (stock) | |
| 1.31.737.501 I Final estimate: PPL = 18.3627 +/- 0.79499 | |
| == opt vs stock | |
| 0.13.393.993 I | |
| chunk PPL ln(PPL(Q)/PPL(base)) KL Divergence Δp RMS Same top p | |
| 6 24.3841 ± 2.5972 0.06191 ± 0.02147 0.06779 ± 0.00974 4.503 ± 0.393 % 91.699 ± 0.706 % | |
| 19 23.9972 ± 1.4052 0.03615 ± 0.00982 0.06924 ± 0.00674 4.672 ± 0.267 % 91.889 ± 0.392 % | |
| 27 17.8997 ± 0.8389 0.02832 ± 0.00775 0.06868 ± 0.00558 5.388 ± 0.294 % 92.433 ± 0.319 % | |
| Mean PPL(Q) : 18.277320 ± 0.787208 | |
| Mean PPL(base) : 17.797758 ± 0.744380 | |
| Cor(ln(PPL(Q)), ln(PPL(base))): 98.68% | |
| Mean ln(PPL(Q)/PPL(base)) : 0.026588 ± 0.007011 | |
| Mean PPL(Q)/PPL(base) : 1.026945 ± 0.007200 | |
| Mean PPL(Q)-PPL(base) : 0.479562 ± 0.131595 | |
| Mean KLD: 0.066583 ± 0.004897 | |
| 99.9% KLD: 7.426280 | |
| 99.0% KLD: 1.112220 | |