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 / HANDOFF.md

HelloSun

HANDOFF: start Q4_1 optimization on this machine (baseline + plan)

b26cee8 verified 3 days ago

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3.27 kB

HANDOFF — flint Q4_1 CPU optimization (this machine)

Updated: 2026-10-31 (UTC+1) — periodic checkpoint (uploaded every ~5 min while working).

Read this first

This is the live handoff for continuing the Q4_1 optimization of HelloSun/flint_cpu_opt. The static docs are README.md, benchmarks/q41-summary.md (Q4_1) and benchmarks/summary.md (Q4_K_XL).

Machine (the one this work targets)

Intel Xeon Platinum 8559C, 2 sockets, 192 logical CPUs online.
cgroup limit: 16 CPUs (cpu.max = 1600000/100000), nproc = 16.
2 TiB RAM host; memory guard: agent-started processes must stay < 80 GiB RSS.
Flags: AVX512-F/BW/VL/DQ, AVX512-VNNI, AVX512-FP16, AMX-INT8/AMX-BF16, F16C.
Same CPU model as the numbers in machine.txt (16 threads), but the host is shared, so single runs vary ~±8 %.
Repo base: llama.cpp de5122d879d00d2895c36be7e0cee94105aeeabd with patches/flint-cpu-opt.patch applied in llama.cpp/.

Current model/server

Model: flint-int4-q4_1.gguf (24.2 GiB, every weight Q4_1; token_embd + router F32), downloaded from RecursiveSI/flint-int4-gguf.
llama-server running on 127.0.0.1:20000, alias flint-q4_1, -t 16 -fa on --jinja -c 32768 (started by build.sh).
pi coding agent is configured to use it (pi-local).

Current Q4_1 kernel state (already in the patch)

ggml_vec_dot_q4_1_q8_1 (decode): AVX512-VNNI, 2 blocks per ymm, 4 blocks/iteration, prefetch GGML_Q41_PF (default 192 blocks, T1 hint).
Tiled VNNI GEMM for Q4_1 prefill: per-32-block f32 sd/sm, Q41 template flag, Q8_1 activations.
Reported on 8559C/16t: pp256 266, pp2048 297, tg128 55.7 t/s; stock 183/189/48.3. KLD inside stock's own noise floor.

Plan / next steps

Read existing docs and machine context.
Build llama-bench + llama-cli (only llama-server was built).
Baseline on this machine: interleaved A/B, taskset -c 0-15, -t 16 -fa on -b 1024 -ub 1024, Q4_1 file.
Sweep GGML_Q41_PF (e.g. 128/192/256/320) and thread counts on the real model (not only microbench).
Try one decode-side improvement (memory-bound path) and/or an AMX prefill variant; keep only if it beats noise.
A/B compare, record raw CSV/log under benchmarks/.
Update README.md + benchmarks/q41-summary.md only with measured, reproducible numbers.
Restart llama-server with the final best settings.

How to reproduce

cd /root/flint_cpu_opt
cmake --build llama.cpp/build -j16 --target llama-bench llama-cli
taskset -c 0-15 llama.cpp/build/bin/llama-bench -m flint-int4-q4_1.gguf \
  -t 16 -fa on -lm mlock -b 1024 -ub 1024 -p 256,2048 -n 128

Notes / gotchas

Do not run 100 parallel Tor/open code probes here; memory guard kills processes over 80 GiB / single child over 64 GiB.
The llama-server is serving pi; stop it before a clean benchmark, restart afterwards (bash build.sh reuses an already-running server).
Host is shared: use medians of >= 3 interleaved rounds; differences under ~8 % are not a win.
Kernel changes must keep integer dot products exact; only f32 accumulation order may change. Check benchmarks/graph_accuracy_test.cpp and KLD if a numeric path changes.