Instructions to use Nitishsharma9/CyberCoder-Mobile-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nitishsharma9/CyberCoder-Mobile-7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Nitishsharma9/CyberCoder-Mobile-7B-GGUF",
	filename="CyberCoder-Mobile-7B-IQ1_S.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 Nitishsharma9/CyberCoder-Mobile-7B-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 Nitishsharma9/CyberCoder-Mobile-7B-GGUF:IQ1_S
# Run inference directly in the terminal:
llama cli -hf Nitishsharma9/CyberCoder-Mobile-7B-GGUF:IQ1_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Nitishsharma9/CyberCoder-Mobile-7B-GGUF:IQ1_S
# Run inference directly in the terminal:
llama cli -hf Nitishsharma9/CyberCoder-Mobile-7B-GGUF:IQ1_S

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 Nitishsharma9/CyberCoder-Mobile-7B-GGUF:IQ1_S
# Run inference directly in the terminal:
./llama-cli -hf Nitishsharma9/CyberCoder-Mobile-7B-GGUF:IQ1_S

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 Nitishsharma9/CyberCoder-Mobile-7B-GGUF:IQ1_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Nitishsharma9/CyberCoder-Mobile-7B-GGUF:IQ1_S

Use Docker

docker model run hf.co/Nitishsharma9/CyberCoder-Mobile-7B-GGUF:IQ1_S

LM Studio
Jan
Ollama
How to use Nitishsharma9/CyberCoder-Mobile-7B-GGUF with Ollama:
```
ollama run hf.co/Nitishsharma9/CyberCoder-Mobile-7B-GGUF:IQ1_S
```

Unsloth Studio

How to use Nitishsharma9/CyberCoder-Mobile-7B-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 Nitishsharma9/CyberCoder-Mobile-7B-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 Nitishsharma9/CyberCoder-Mobile-7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Nitishsharma9/CyberCoder-Mobile-7B-GGUF to start chatting

How to use Nitishsharma9/CyberCoder-Mobile-7B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Nitishsharma9/CyberCoder-Mobile-7B-GGUF:IQ1_S

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": "Nitishsharma9/CyberCoder-Mobile-7B-GGUF:IQ1_S"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Nitishsharma9/CyberCoder-Mobile-7B-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 Nitishsharma9/CyberCoder-Mobile-7B-GGUF:IQ1_S

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 Nitishsharma9/CyberCoder-Mobile-7B-GGUF:IQ1_S

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Nitishsharma9/CyberCoder-Mobile-7B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Nitishsharma9/CyberCoder-Mobile-7B-GGUF:IQ1_S

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 "Nitishsharma9/CyberCoder-Mobile-7B-GGUF:IQ1_S" \
  --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 Nitishsharma9/CyberCoder-Mobile-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/Nitishsharma9/CyberCoder-Mobile-7B-GGUF:IQ1_S
```

Lemonade

How to use Nitishsharma9/CyberCoder-Mobile-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Nitishsharma9/CyberCoder-Mobile-7B-GGUF:IQ1_S

Run and chat with the model

lemonade run user.CyberCoder-Mobile-7B-GGUF-IQ1_S

List all available models

lemonade list

CyberCoder-Mobile-7B-GGUF

GGUF quantizations of Qwen2.5-Coder-7B-Instruct, a powerful 7B parameter model specifically fine-tuned for incredibly fast Python scripting, Fill-in-the-Middle (FIM) code completion, Ethical Hacking, and Cybersecurity operations.

Background

This model serves as a general-purpose reasoning distill specifically tailored for offensive and defensive security contexts. By taking the state-of-the-art Qwen 2.5 Coder 7B base, this model delivers massive reasoning capabilities compressed into a footprint that can run natively on mobile devices (via PocketPal) and PCs with 6GB+ RAM.

Core Capabilities:

⚡ Lightning Fast Python Scripting: Optimized to generate robust, production-ready Python tools in milliseconds.
🛡️ Ethical Hacking & Cyber Security: Deep knowledge of vulnerability assessment, penetration testing patterns, and defensive engineering.
🔄 Fill-in-the-Middle (FIM): Native support for seamless code completion right inside your IDE.

Hardware compatibility (Ultra-Compressed IQ Formats)

Quantization	Bits	Exact File Size	RAM Required
`IQ1_S`	1.56-bit	~1.5 GB	~2.5 GB
`IQ2_XXS`	2.06-bit	~1.9 GB	~3.0 GB
`IQ2_S`	2.50-bit	~2.3 GB	~3.5 GB
`IQ3_XXS`	3.06-bit	~2.8 GB	~4.0 GB
`Q4_K_M`	4.00-bit	4.68 GB	~5.7 GB

🚀 How to Use

📱 PocketPal (Mobile)

Download the PocketPal app on your iOS or Android device.
Navigate to Models -> Add Model -> Hugging Face.
Search for Nitishsharma9/CyberCoder-Mobile-7B-GGUF.
Download the Q2_K or Q3_K_M file (these are the best sizes for mobile RAM limits).
Load the model and start chatting entirely offline!

🦙 Ollama (PC/Mac/Linux)

You can run this natively in Ollama. Download your preferred GGUF file (e.g., CyberCoder-Mobile-7B-Q4_K_M.gguf), then create a file named Modelfile with this content:

FROM ./CyberCoder-Mobile-7B-Q4_K_M.gguf

Then build and run:

ollama create CyberCoder -f Modelfile
ollama run CyberCoder

💻 LM Studio / AnythingLLM

Open LM Studio or your preferred desktop application.
Search for Nitishsharma9/CyberCoder-Mobile-7B-GGUF in the search bar.
Select your desired quantization (Q4_K_M is highly recommended for 6GB RAM PCs).
Click Download and Load the model!

Downloads last month: 250

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

1-bit

Inference Providers NEW

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