Instructions to use Nitishsharma9/super-lite-model-upload with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nitishsharma9/super-lite-model-upload with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Nitishsharma9/super-lite-model-upload",
	filename="super-lite-cyber-coder-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 Nitishsharma9/super-lite-model-upload 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/super-lite-model-upload:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Nitishsharma9/super-lite-model-upload:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Nitishsharma9/super-lite-model-upload:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Nitishsharma9/super-lite-model-upload: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 Nitishsharma9/super-lite-model-upload:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Nitishsharma9/super-lite-model-upload: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 Nitishsharma9/super-lite-model-upload:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Nitishsharma9/super-lite-model-upload:Q4_K_M

Use Docker

docker model run hf.co/Nitishsharma9/super-lite-model-upload:Q4_K_M

LM Studio
Jan
Ollama
How to use Nitishsharma9/super-lite-model-upload with Ollama:
```
ollama run hf.co/Nitishsharma9/super-lite-model-upload:Q4_K_M
```

Unsloth Studio

How to use Nitishsharma9/super-lite-model-upload 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/super-lite-model-upload 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/super-lite-model-upload to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Nitishsharma9/super-lite-model-upload to start chatting

How to use Nitishsharma9/super-lite-model-upload with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Nitishsharma9/super-lite-model-upload: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": "Nitishsharma9/super-lite-model-upload:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Nitishsharma9/super-lite-model-upload 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/super-lite-model-upload: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 Nitishsharma9/super-lite-model-upload:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Nitishsharma9/super-lite-model-upload with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Nitishsharma9/super-lite-model-upload: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 "Nitishsharma9/super-lite-model-upload: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 Nitishsharma9/super-lite-model-upload with Docker Model Runner:
```
docker model run hf.co/Nitishsharma9/super-lite-model-upload:Q4_K_M
```

Lemonade

How to use Nitishsharma9/super-lite-model-upload with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Nitishsharma9/super-lite-model-upload:Q4_K_M

Run and chat with the model

lemonade run user.super-lite-model-upload-Q4_K_M

List all available models

lemonade list

Super-Lite Cyber Coder 1.5B (GGUF)

This is a highly optimized, super-lite language model designed for Clean, secure code generation, debugging, and authorized ethical hacking & penetration testing methodologies. It has been fine-tuned on coding and cybersecurity datasets, maintaining strict adherence to defensive and educational boundaries.

Target Hardware

RAM Requirement: < 4GB
Format: GGUF (Q4_K_M)
Size: < 1.2 GB
Context Length: 2048 Tokens

How to use with PocketPal AI (Mobile)

Download PocketPal AI from your device's app store.
Load the super-lite-cyber-coder-q4_k_m.gguf file directly into the app.
Configure the chat template to use ChatML (<|im_start|> / <|im_end|>).
Set the system prompt to enforce safe, authorized penetration testing boundaries.

How to use with LM Studio / Ollama

Ollama: Since you're on Windows, here’s the exact command to download and run it with Ollama:
ollama run hf.co/Nitishsharma9/super-lite-model-upload:Q4_K_M:

What this command does:

ollama run: This tells Ollama to run a model.

hf.co/...: This is the special identifier that tells Ollama to look for the model on Hugging Face (hf.co is the short URL for huggingface.co).:
Nitishsharma9/super-lite-model-upload: This is the repository path on Hugging Face.:
:Q4_K_M: This specifies the exact quantization file (the .gguf file) to use, which matches the file you were trying to use earlier.:
When you run this command for the first time, Ollama will automatically download the model file from Hugging Face and store it locally. After the download finishes, you'll be placed directly into an interactive chat session with the model.:

Downloads last month: 79

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

4-bit

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