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

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 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

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

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 @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": "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 Aug 1

Commit

fc6e637

verified ·

1 Parent(s): 2c13112

Model card: add v6 MCQ results + v6cti CTI-round experiment note

Browse files

Files changed (1) hide show

README.md +18 -6

README.md CHANGED Viewed

@@ -62,12 +62,15 @@ 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-sec-8b v4 | k3-sec-8b v5 |
-|---|---:|---:|---:|
-| CyberMetric-500 | 84.20% | **86.80%** | **86.80%** |
-| CTI-Bench-MCQ (500) | **66.00%** | 59.20% | 59.80% |
-| SecQA v1+v2 (210) | 98.10% | 98.10% | 98.10% |
-| **Overall (1,210)** | **79.09%** | 77.36% | 77.60% |
 Parse failures: 0 for all models in native modes.
@@ -88,6 +91,15 @@ mostly-correct completion-style answers (~152 words avg); k3 v4/v5 give
 structured markdown answers (~264/~307 words avg) with finer-grained precision
 (e.g. LSASS dumping → T1003.001 sub-technique + detection methods, vs T1003 only).
 **Takeaways.** k3-sec-8b (v4/v5) is stronger on broad general security knowledge
 (CyberMetric) and far more usable interactively; Foundation-Sec-8B is stronger on
 MITRE ATT&CK / threat-intel mapping (CTI-Bench) — the clearest data target for

 (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-sec-8b v4 | k3-sec-8b v5 | k3-sec-8b v6 |
+|---|---:|---:|---:|---:|
+| CyberMetric-500 | 84.20% | **86.80%** | **86.80%** | **86.80%** |
+| CTI-Bench-MCQ (500) | **66.00%** | 59.20% | 59.80% | 58.80% |
+| SecQA v1+v2 (210) | 98.10% | 98.10% | 98.10% | 98.10% |
+| **Overall (1,210)** | **79.09%** | 77.36% | 77.60% | 77.44% |
+*v5 → v6 is knowledge-neutral on MCQ (r6 targeted agentic robustness). Few-shot
+completion mode (base-model style) lifts all k3 versions: v6 scores 87.6/62.8/98.1.*
 Parse failures: 0 for all models in native modes.
 structured markdown answers (~264/~307 words avg) with finer-grained precision
 (e.g. LSASS dumping → T1003.001 sub-technique + detection methods, vs T1003 only).
+**CTI round experiment (v6cti branch, 2026-08-01):** a 2,521-trace ATT&CK-STIX-derived
+MCQ seed factory (decontaminated vs all three benchmarks, GLM-5.2 + Foundation-Sec
+teachers, deepseek-v4-flash judge) was trained on top of the r6 mix. Result: large
+CTI-Bench churn (+34/−37 flips) but no net gain and ~1.5 pt chat-mode regression —
+format conflict between justification-style traces and letter-only prompting was the
+main diagnosed cause. Negative result documented in the eval report; v2 plan is
+format-matched MCQ training + failure-targeted DPO. The `v6cti` branch remains
+available for inspection.
 **Takeaways.** k3-sec-8b (v4/v5) is stronger on broad general security knowledge
 (CyberMetric) and far more usable interactively; Foundation-Sec-8B is stronger on
 MITRE ATT&CK / threat-intel mapping (CTI-Bench) — the clearest data target for