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

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

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 / benchmarks /nvfp4 /bench_run.sh

HelloSun

Add NVFP4 CPU kernels results, integrate model card, add handoff

45f1d0b verified 4 days ago

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563 Bytes

#!/bin/bash
# interleaved A/B: each round runs stock, patched-before, patched-after for NVFP4 only
M=/root/flint_cpu_opt/flint-nvfp4.gguf
OUT=/tmp/opencode/bench
for R in 1 2 3; do
  for B in bin_stock bin_patched_before bin_ppl_new; do
    LD_LIBRARY_PATH=/tmp/opencode/$B taskset -c 0-15 /tmp/opencode/$B/llama-bench -m $M -t 16 -fa 1 -b 1024 -ub 1024 -lm mmap+mlock -p 256,2048 -n 128 -r 3 -o csv > $OUT/${B}_r$R.csv 2> $OUT/${B}_r$R.err
    echo "$B round$R exit $? $(uptime | sed 's/.*average://')" >> $OUT/done.log
  done
done
echo ALLDONE >> $OUT/done.log

	#!/bin/bash
	# interleaved A/B: each round runs stock, patched-before, patched-after for NVFP4 only
	M=/root/flint_cpu_opt/flint-nvfp4.gguf
	OUT=/tmp/opencode/bench
	for R in 1 2 3; do
	for B in bin_stock bin_patched_before bin_ppl_new; do
	LD_LIBRARY_PATH=/tmp/opencode/$B taskset -c 0-15 /tmp/opencode/$B/llama-bench -m $M -t 16 -fa 1 -b 1024 -ub 1024 -lm mmap+mlock -p 256,2048 -n 128 -r 3 -o csv > $OUT/${B}_r$R.csv 2> $OUT/${B}_r$R.err
	echo "$B round$R exit $? $(uptime \| sed 's/.*average://')" >> $OUT/done.log
	done
	done
	echo ALLDONE >> $OUT/done.log