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 /q41-summary.md

HelloSun

Add Q4_1 tiled VNNI GEMM for prefill: pp256 +34%, pp2048 +42% vs vec_dot patch

616131c verified 6 days ago

preview code

Raw History Blame Contribute Delete

3.71 kB

RecursiveSI/flint-int4-gguf (flint-int4-q4_1.gguf) with this patch

The Q4_1 file (24.2 GiB, every weight tensor Q4_1; token_embd and the router are F32) is not hosted here; download it from RecursiveSI/flint-int4-gguf. The patch adds an AVX512-VNNI vec_dot for Q4_1 x Q8_1 (the x86 path had AVX2 only) and puts Q4_1 into the tiled VNNI GEMM used for prefill.

llama-bench -t 16 -fa on -lm mlock -b 1024 -ub 1024, 3 interleaved rounds, median t/s (raw: q41-ab-interleaved.csv for the vec_dot kernel, q41-tiled-ab-interleaved.csv for the tiled GEMM), Xeon Platinum 8559C, same host as the other benchmarks (shared, +-8 % per run).

	pp256	pp2048	tg128
stock llama.cpp (de5122d)	182.7	188.7	48.3
this patch, vec_dot only	198.8	208.5	54.0
this patch, tiled GEMM	266.5	297.2	55.7
gain vs stock	+46 %	+57 %	+15 %
gain vs vec_dot only	+34 %	+42 %	+3 % (noise)

Decode is unchanged by the tiled path (single-row work stays below the tiled row threshold); the small tg difference is noise.

Tiled Q4_1 GEMM (prefill)

Q4_1 tiles carry no super-block scale: each 32-wide block of a 256-element slab has its own f32 d (scale) and m (min). The tiled kernel gains sd/sm arrays (per-32-block f32, indexed per tile row) and a Q41 template flag; activations use the existing Q8_1 vec_dot_type, so no new quantizer was needed. Per 32-block the kernel computes d_a * (d_w * dot(q, code) + m_w * sum(code)): the integer dot and integer code sum are exact and only the f32 accumulation order differs from vec_dot. VNNI uses vpdpbusd with the 0..15 weight nibbles as the unsigned side. The driver scales the k-block byte offset by 256/blck_size (8 for Q4_1) instead of assuming one block per 256 elements.

vec_dot kernel (decode)

Two 32-wide blocks per iteration. The two blocks' nibbles are packed into one ymm with memory-source vinserti128 (no shuffle-port work), low and high nibbles each go through one vpdpbusd against the matching Q8_1 halves, the four fp16 header values of the two blocks are converted at once, and the per-block d*dy scale is spread with a single vpermps. A 512-bit version that re-ordered nibbles with cross-lane shuffles was slower than stock (shuffle-port bound) and was dropped. Prefetch distance GGML_Q41_PF (blocks, default 192; 192-320 equal in the cold-MoE microbenchmark). Microbenchmarks (16 threads, K=2048): cache-resident mul_mat 183 -> 220 GB/s, cold MoE decode (mul_mat_id) 43 -> 57 GB/s.

Accuracy

wikitext-2 test, 32 x 512, tiled Q4_1 vs the vec_dot path of the same patch (logits baseline):

	KLD	same top-1
tiled Q4_1 vs vec_dot (`q41-tiled-kld.log`)	0.0372 +- 0.0026	94.2 %
vec_dot patch vs stock	0.0364 +- 0.0029	93.8 %
stock with `-fa off` (noise floor)	0.0385 +- 0.0027	93.9 %

The tiled path is inside the patch's own noise floor. Graph-level mul_mat / mul_mat_id error versus a float reference is identical for tiled and vec_dot (5.29e-3 vs 5.29e-3 for T=1, 5.35e-3 vs 5.39e-3 for T=64; Q8_0 reference 5.35e-3). Integer dot products are exact; only float accumulation order differs.

Versus the Q4_K_XL file (same patch, same host)

file	pp256	pp2048	tg128
flint-UD-Q4_K_XL (25.1 GB), patched	296	307	42.8
flint-int4-q4_1 (26.0 GB), patched	266	297	55.7

Q4_1 decodes about 30 % faster (0.625 B/weight and a simple layout) and now prefills within about 10 % of Q4_K_XL (the tiled Q4_1 GEMM closed most of the earlier gap). PPL of the two files is similar (18.26 vs 18.36 stock).