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

Notebooks
Google Colab
Kaggle
Local Apps Settings

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

Use Docker

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

LM Studio
Jan

vLLM

How to use ahmedandaloes/CyberStrike-OffSec-35B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ahmedandaloes/CyberStrike-OffSec-35B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ahmedandaloes/CyberStrike-OffSec-35B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

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

Unsloth Studio

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

How to use ahmedandaloes/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 ahmedandaloes/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": "ahmedandaloes/CyberStrike-OffSec-35B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ahmedandaloes/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 ahmedandaloes/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 ahmedandaloes/CyberStrike-OffSec-35B-GGUF:Q4_K_M

Run Hermes

hermes

OpenClaw new

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

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ahmedandaloes/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

Atomic Chat

CyberStrike-OffSec-35B — GGUF

GGUF quantizations of oyildirim/CyberStrike-OffSec-35B (qwen3_5_moe, Qwen3.5 MoE) for llama.cpp, Ollama, LM Studio, etc. Text-only conversion.

Each quant is split into ~5 GB shards (-0000N-of-0000M.gguf). Download all shards of your chosen quant — llama.cpp/Ollama load them automatically from the first shard.

Quant options

Quant	Bits (approx)	Use
Q2_K	~2.6	smallest, lowest RAM, lossy
Q3_K_S / Q3_K_M / Q3_K_L	~3	small, budget RAM
Q4_K_S / Q4_K_M	~4	recommended balance
Q5_K_S / Q5_K_M	~5	higher quality
Q6_K	~6	near-lossless
Q8_0	8	highest fidelity GGUF

For MLX (Apple Silicon) builds see the MLX collection.

Usage (llama.cpp)

# download all shards of one quant (e.g. Q4_K_M), then:
./llama-cli -m CyberStrike-OffSec-35B-Q4_K_M-00001-of-0000N.gguf -p "What is SQL injection?"

Usage (Ollama)

# after downloading a quant's shards:
printf 'FROM ./CyberStrike-OffSec-35B-Q4_K_M-00001-of-0000N.gguf\n' > Modelfile
ollama create cyberstrike -f Modelfile
ollama run cyberstrike

Attribution & license

Source: oyildirim/CyberStrike-OffSec-35B · base Qwen/Qwen3.6-35B-A3B
License Apache-2.0 (inherited). Credit to the original author; GGUF builds for the community.
Intended for authorized offensive-security work only.

Downloads last month: 7,043

GGUF

Model size

36B params

Architecture

qwen35moe

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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

Base model

Qwen/Qwen3.6-35B-A3B

Finetuned

oyildirim/CyberStrike-OffSec-35B

Quantized

(6)

this model