Instructions to use oyildirim/CyberStrike-OffSec-35B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use oyildirim/CyberStrike-OffSec-35B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="oyildirim/CyberStrike-OffSec-35B-GGUF",
	filename="CyberStrike-OffSec-35B-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use oyildirim/CyberStrike-OffSec-35B-GGUF 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 oyildirim/CyberStrike-OffSec-35B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf oyildirim/CyberStrike-OffSec-35B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf oyildirim/CyberStrike-OffSec-35B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf oyildirim/CyberStrike-OffSec-35B-GGUF: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 oyildirim/CyberStrike-OffSec-35B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf oyildirim/CyberStrike-OffSec-35B-GGUF: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 oyildirim/CyberStrike-OffSec-35B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf oyildirim/CyberStrike-OffSec-35B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/oyildirim/CyberStrike-OffSec-35B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use oyildirim/CyberStrike-OffSec-35B-GGUF with Ollama:
```
ollama run hf.co/oyildirim/CyberStrike-OffSec-35B-GGUF:Q4_K_M
```

Unsloth Studio

How to use oyildirim/CyberStrike-OffSec-35B-GGUF 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 oyildirim/CyberStrike-OffSec-35B-GGUF 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 oyildirim/CyberStrike-OffSec-35B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for oyildirim/CyberStrike-OffSec-35B-GGUF to start chatting

How to use oyildirim/CyberStrike-OffSec-35B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf oyildirim/CyberStrike-OffSec-35B-GGUF: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": "oyildirim/CyberStrike-OffSec-35B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use oyildirim/CyberStrike-OffSec-35B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf oyildirim/CyberStrike-OffSec-35B-GGUF: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 oyildirim/CyberStrike-OffSec-35B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use oyildirim/CyberStrike-OffSec-35B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf oyildirim/CyberStrike-OffSec-35B-GGUF: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 "oyildirim/CyberStrike-OffSec-35B-GGUF: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"

Docker Model Runner
How to use oyildirim/CyberStrike-OffSec-35B-GGUF with Docker Model Runner:
```
docker model run hf.co/oyildirim/CyberStrike-OffSec-35B-GGUF:Q4_K_M
```

Lemonade

How to use oyildirim/CyberStrike-OffSec-35B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull oyildirim/CyberStrike-OffSec-35B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.CyberStrike-OffSec-35B-GGUF-Q4_K_M

List all available models

lemonade list

⚠️ Deprecated — known-broken build

These GGUFs are an earlier CyberStrike build with known tool-calling defects (wrong/malformed calls, loops, and hallucinated tool outputs) caused by training data in the wrong tool-call format. Access is gated to prevent accidental use. For the current, fixed model use oyildirim/CyberStrike-OffSec-35B.

⚠️ DEPRECATED — CyberStrike-OffSec-35B GGUF (known issues)

This version has known defects. Do not use for new deployments.

Root cause — the training data was in the wrong format

This (previous, merged) model's SFT dataset taught tool calls in a format the model never learned to emit as real structured calls. As a result it produced wrong / malformed tool calls, fell into loops, and — unable to actually invoke a tool — hallucinated: it wrote tool calls as prose (**Action 1:** Task(GHOST…)) and invented its own tool outputs (fake curl/SSL handshakes, Nmap scans, Set-Cookie: sessionid=abc…, even fabricated flags), never terminating — in both bf16 and q8. It looked like it was working while executing nothing. The "8-bit GGUF broken" reports trace to the model itself, not quantization.

Verified by a three-way A/B

A controlled evaluation compared base Qwen3.6 vs. this previous model vs. the current model over 24 scenarios (6 axes, 62% out-of-distribution). This model scored 0/24 genuine structured tool calls and 3/24 clean termination with widespread fabrication; the current model scores 18/24 and 24/24 with no fabrication. Full methodology, prompts, matrix, and raw quotes: EVALUATION.md.

👉 Use the current model instead

oyildirim/CyberStrike-OffSec-35B — single-piece merged checkpoint, verified via the one-command Python load (see its model card for serving details):

from transformers import AutoModelForImageTextToText, AutoTokenizer
model = AutoModelForImageTextToText.from_pretrained("oyildirim/CyberStrike-OffSec-35B")
tok   = AutoTokenizer.from_pretrained("oyildirim/CyberStrike-OffSec-35B")

The files below are retained (gated) only to preserve history and existing references. A clean q8 GGUF of the current model is not yet available (adapter-based; q8 pipeline deferred to Stage-2).

CyberStrike-OffSec-35B-Q4_K_M.gguf · -Q5_K_M · -Q6_K · -Q8_0 — deprecated / broken build

Downloads last month: 5,544

GGUF

Model size

36B params

Architecture

qwen35moe

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for oyildirim/CyberStrike-OffSec-35B-GGUF

Base model

Qwen/Qwen3.6-35B-A3B

Quantized

(658)

this model