Instructions to use bebrws/security-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use bebrws/security-8b 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 bebrws/security-8b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bebrws/security-8b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf bebrws/security-8b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bebrws/security-8b: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 bebrws/security-8b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bebrws/security-8b: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 bebrws/security-8b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bebrws/security-8b:Q4_K_M

Use Docker

docker model run hf.co/bebrws/security-8b:Q4_K_M

LM Studio
Jan
Ollama
How to use bebrws/security-8b with Ollama:
```
ollama run hf.co/bebrws/security-8b:Q4_K_M
```

Unsloth Studio

How to use bebrws/security-8b 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 bebrws/security-8b 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 bebrws/security-8b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for bebrws/security-8b to start chatting

How to use bebrws/security-8b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bebrws/security-8b:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "bebrws/security-8b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use bebrws/security-8b with Docker Model Runner:
```
docker model run hf.co/bebrws/security-8b:Q4_K_M
```

Lemonade

How to use bebrws/security-8b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bebrws/security-8b:Q4_K_M

Run and chat with the model

lemonade run user.security-8b-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use bebrws/security-8b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bebrws/security-8b: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 bebrws/security-8b:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use bebrws/security-8b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bebrws/security-8b: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 "bebrws/security-8b: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"

bebrws commited on 23 days ago

Commit

4a148a8

verified ·

1 Parent(s): e39d5ab

Model card: benchmark vs Foundation-Sec-1.1-8B-Instruct (honest standings)

Browse files

Files changed (1) hide show

README.md +11 -6

README.md CHANGED Viewed

@@ -63,12 +63,17 @@ Foundation-Sec-8B is a *base* model and was evaluated with 3-shot raw completion
 (its intended usage per Cisco's model card); k3-sec-8b was evaluated zero-shot
 chat (Qwen3 template, non-thinking). Identical question sets for every model.
-| Benchmark (n) | Foundation-Sec-8B | k3 v4 | v5 | v6 | v7cti (chat) | v7cti (completion) |
-|---|---:|---:|---:|---:|---:|---:|
-| CyberMetric-500 | 84.20% | **86.80%** | **86.80%** | **86.80%** | 86.20% | **87.80%** |
-| CTI-Bench-MCQ (500) | **66.00%** | 59.20% | 59.80% | 58.80% | 60.40% | 62.60% |
-| SecQA v1+v2 (210) | 98.10% | 98.10% | 98.10% | 98.10% | 97.62% | **98.57%** |
-| **Overall (1,210)** | **79.09%** | 77.36% | 77.60% | 77.19% | 77.52% | **79.26%** |
 Parse failures: 0 for all models in native modes.

 (its intended usage per Cisco's model card); k3-sec-8b was evaluated zero-shot
 chat (Qwen3 template, non-thinking). Identical question sets for every model.
+| Benchmark (n) | F-Sec-8B (base) | F-Sec-1.1-8B-Instruct | k3 v6 | v7cti (chat) | v7cti (completion) |
+|---|---:|---:|---:|---:|---:|
+| CyberMetric-500 | 84.20% | 85.80% | **86.80%** | 86.20% | **87.80%** |
+| CTI-Bench-MCQ (500) | 66.00% | **67.40%** | 58.80% | 60.40% | 62.60% |
+| SecQA v1+v2 (210) | 98.10% | **99.05%** | 98.10% | 97.62% | 98.57% |
+| **Overall (1,210)** | 79.09% | **80.50%** | 77.19% | 77.52% | 79.26% |
+Note: `Foundation-Sec-8B` is a *base* model (few-shot completion is its only usable
+mode; zero-shot chat collapses to ~34%). The fair chat-era comparator is
+`Foundation-Sec-1.1-8B-Instruct` (both modes shown: chat/completion 85.0/85.8,
+65.8/67.4, 98.6/99.1).
 Parse failures: 0 for all models in native modes.