Instructions to use bytecode66/froged_N2.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use bytecode66/froged_N2.1 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 bytecode66/froged_N2.1:Q8_0
# Run inference directly in the terminal:
llama cli -hf bytecode66/froged_N2.1:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf bytecode66/froged_N2.1:Q8_0
# Run inference directly in the terminal:
llama cli -hf bytecode66/froged_N2.1:Q8_0

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 bytecode66/froged_N2.1:Q8_0
# Run inference directly in the terminal:
./build/bin/llama cli -hf bytecode66/froged_N2.1:Q8_0

Use Docker

docker model run hf.co/bytecode66/froged_N2.1:Q8_0

Llama new
LM Studio
Jan

vLLM

How to use bytecode66/froged_N2.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bytecode66/froged_N2.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bytecode66/froged_N2.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bytecode66/froged_N2.1:Q8_0

Ollama
How to use bytecode66/froged_N2.1 with Ollama:
```
ollama run hf.co/bytecode66/froged_N2.1:Q8_0
```
Unsloth Desktop

How to use bytecode66/froged_N2.1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bytecode66/froged_N2.1:Q8_0

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": "bytecode66/froged_N2.1:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use bytecode66/froged_N2.1 with Docker Model Runner:
```
docker model run hf.co/bytecode66/froged_N2.1:Q8_0
```

Lemonade

How to use bytecode66/froged_N2.1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bytecode66/froged_N2.1:Q8_0

Run and chat with the model

lemonade run user.froged_N2.1-Q8_0

List all available models

lemonade list

Hermes Agent

How to use bytecode66/froged_N2.1 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bytecode66/froged_N2.1:Q8_0

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 bytecode66/froged_N2.1:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use bytecode66/froged_N2.1 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bytecode66/froged_N2.1:Q8_0

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 "bytecode66/froged_N2.1:Q8_0" \
  --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"

froged_N2.1

File size: 1,427 Bytes

d74fc0e

---
tags:
  - gguf
  - llama.cpp
  - text-generation
  - cybersecurity
  - devops
  - coding
base_model: WhiteRabbitNeo/WhiteRabbitNeo-V3-7B
pipeline_tag: text-generation
---

# Froged_N2.1

Repositori ini berisi kuantisasi **Q8_0 GGUF** dari WhiteRabbitNeo V3 7B, disalin dari [bartowski/WhiteRabbitNeo_WhiteRabbitNeo-V3-7B-GGUF](https://huggingface.co/bartowski/WhiteRabbitNeo_WhiteRabbitNeo-V3-7B-GGUF). Ukuran berkas sekitar 8,1 GB. Berkas ini ditujukan untuk runtime GGUF seperti `llama.cpp`; model chat berjalan pada perangkat atau server dengan memori yang memadai.

## Menjalankan dengan llama.cpp

```bash
llama-cli -hf bytecode66/froged_N2.1:WhiteRabbitNeo_WhiteRabbitNeo-V3-7B-Q8_0.gguf
```

Untuk membuka API lokal yang kompatibel dengan OpenAI:

```bash
llama-server -hf bytecode66/froged_N2.1:WhiteRabbitNeo_WhiteRabbitNeo-V3-7B-Q8_0.gguf --host 0.0.0.0 --port 8080
```

Gunakan hanya untuk pembelajaran dan pengujian keamanan yang Anda miliki atau telah mendapat izin, termasuk lab dan CTF. Jangan gunakan untuk mengakses atau mengganggu sistem tanpa izin.

Model sumber dan detail kuantisasi: [bartowski/WhiteRabbitNeo_WhiteRabbitNeo-V3-7B-GGUF](https://huggingface.co/bartowski/WhiteRabbitNeo_WhiteRabbitNeo-V3-7B-GGUF). Model dasar: [WhiteRabbitNeo/WhiteRabbitNeo-V3-7B](https://huggingface.co/WhiteRabbitNeo/WhiteRabbitNeo-V3-7B). Periksa lisensi pada repositori sumber sebelum penggunaan atau redistribusi.