Instructions to use cycloevan/vuln_detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cycloevan/vuln_detector with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="cycloevan/vuln_detector")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("cycloevan/vuln_detector", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use cycloevan/vuln_detector 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 cycloevan/vuln_detector:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf cycloevan/vuln_detector:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf cycloevan/vuln_detector:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf cycloevan/vuln_detector:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf cycloevan/vuln_detector:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf cycloevan/vuln_detector:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf cycloevan/vuln_detector:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cycloevan/vuln_detector:Q4_K_M

Use Docker

docker model run hf.co/cycloevan/vuln_detector:Q4_K_M

LM Studio
Jan

vLLM

How to use cycloevan/vuln_detector with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cycloevan/vuln_detector"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cycloevan/vuln_detector",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cycloevan/vuln_detector:Q4_K_M

SGLang

How to use cycloevan/vuln_detector with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "cycloevan/vuln_detector" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cycloevan/vuln_detector",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "cycloevan/vuln_detector" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cycloevan/vuln_detector",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use cycloevan/vuln_detector with Ollama:
```
ollama run hf.co/cycloevan/vuln_detector:Q4_K_M
```

Unsloth Studio

How to use cycloevan/vuln_detector with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for cycloevan/vuln_detector to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for cycloevan/vuln_detector to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for cycloevan/vuln_detector to start chatting

How to use cycloevan/vuln_detector with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf cycloevan/vuln_detector:Q4_K_M

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": "cycloevan/vuln_detector:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use cycloevan/vuln_detector with Docker Model Runner:
```
docker model run hf.co/cycloevan/vuln_detector:Q4_K_M
```

Lemonade

How to use cycloevan/vuln_detector with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cycloevan/vuln_detector:Q4_K_M

Run and chat with the model

lemonade run user.vuln_detector-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use cycloevan/vuln_detector with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf cycloevan/vuln_detector:Q4_K_M

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 cycloevan/vuln_detector:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use cycloevan/vuln_detector with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf cycloevan/vuln_detector:Q4_K_M

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 "cycloevan/vuln_detector:Q4_K_M" \
  --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"

cycloevan commited on Jul 10

Commit

7c697d6

verified ·

1 Parent(s): 31fb51f

Model card: expand measured performance with end-to-end runtime comparison (PyTorch vs llama.cpp) and bandwidth/compute-bound explanation

Browse files

Files changed (1) hide show

README.md +15 -2

README.md CHANGED Viewed

@@ -155,7 +155,9 @@ Quantization does not degrade benchmark quality — Q8_0 and Q4_K_M match or sli
 Note: these absolute numbers are not directly comparable to the "Evaluation Results" table above, which used a different sampling/decoding protocol.
-### Measured performance (Apple M1, Metal, `llama-bench`)
 | Variant | Prompt processing (pp512) | Generation (tg128) |
 |---------|--------------------------:|-------------------:|
@@ -163,7 +165,18 @@ Note: these absolute numbers are not directly comparable to the "Evaluation Resu
 | GGUF Q8_0   | 618 t/s | 29.4 t/s |
 | GGUF Q4_K_M | 504 t/s | 42.3 t/s |
-Q4_K_M generates **2.2× faster than F16** (generation is memory-bandwidth-bound, so smaller weights win; prompt processing is compute-bound and favors F16). End-to-end on the 100-sample eval batch, llama.cpp Q4_K_M finished in 53 s vs 138 s for `transformers` fp16 on MPS (**2.6× faster**). Weight memory footprint: 2.48 GB (F16) → 0.81 GB (Q4_K_M).
 ### Run with llama.cpp

 Note: these absolute numbers are not directly comparable to the "Evaluation Results" table above, which used a different sampling/decoding protocol.
+### Measured performance (Apple M1, Metal)
+`llama-bench` (pp512 = prompt processing, tg128 = generation):
 | Variant | Prompt processing (pp512) | Generation (tg128) |
 |---------|--------------------------:|-------------------:|
 | GGUF Q8_0   | 618 t/s | 29.4 t/s |
 | GGUF Q4_K_M | 504 t/s | 42.3 t/s |
+Generation is memory-bandwidth-bound, so smaller weights decode faster: **Q4_K_M generates 2.2× faster than F16**. Prompt processing is compute-bound and favors F16 (dequantization overhead), but at 500+ t/s a typical prompt is prefilled in well under a second — end-to-end latency is dominated by generation, so Q4_K_M is the fastest overall.
+End-to-end wall-clock on the identical 100-sample eval batch (greedy, `n_predict=128`, same prompts):
+| Runtime | Batch time | Speedup |
+|---------|-----------:|--------:|
+| `transformers` fp16 (PyTorch, MPS) | 138 s | 1.0× |
+| llama.cpp GGUF F16    | 96 s | 1.4× |
+| llama.cpp GGUF Q8_0   | 57 s | 2.4× |
+| llama.cpp GGUF Q4_K_M | 53 s | **2.6×** |
+Weight memory footprint: 2.48 GB (F16) → 1.32 GB (Q8_0) → 0.81 GB (Q4_K_M).
 ### Run with llama.cpp