Instructions to use viperprojects47/viper-ai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use viperprojects47/viper-ai 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 viperprojects47/viper-ai
# Run inference directly in the terminal:
llama cli -hf viperprojects47/viper-ai

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf viperprojects47/viper-ai
# Run inference directly in the terminal:
llama cli -hf viperprojects47/viper-ai

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 viperprojects47/viper-ai
# Run inference directly in the terminal:
./llama-cli -hf viperprojects47/viper-ai

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 viperprojects47/viper-ai
# Run inference directly in the terminal:
./build/bin/llama-cli -hf viperprojects47/viper-ai

Use Docker

docker model run hf.co/viperprojects47/viper-ai

LM Studio
Jan

vLLM

How to use viperprojects47/viper-ai with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "viperprojects47/viper-ai"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "viperprojects47/viper-ai",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/viperprojects47/viper-ai

Ollama
How to use viperprojects47/viper-ai with Ollama:
```
ollama run hf.co/viperprojects47/viper-ai
```

Unsloth Studio

How to use viperprojects47/viper-ai 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 viperprojects47/viper-ai 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 viperprojects47/viper-ai to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for viperprojects47/viper-ai to start chatting

How to use viperprojects47/viper-ai with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf viperprojects47/viper-ai

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": "viperprojects47/viper-ai"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use viperprojects47/viper-ai with Docker Model Runner:
```
docker model run hf.co/viperprojects47/viper-ai
```

Lemonade

How to use viperprojects47/viper-ai with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull viperprojects47/viper-ai

Run and chat with the model

lemonade run user.viper-ai-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use viperprojects47/viper-ai with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf viperprojects47/viper-ai

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 viperprojects47/viper-ai

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use viperprojects47/viper-ai with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf viperprojects47/viper-ai

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 "viperprojects47/viper-ai" \
  --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"

Viper AI — 7B

Viper is a custom language model based on Qwen2.5-7B-Instruct, fine-tuned by viperprojects47 for custom software development assistance. It is distributed as a 4-bit quantized GGUF for use with llama.cpp, Ollama, and any OpenAI-compatible server.

Files

File	Size	Quant	Description
`viper.gguf`	4.4GB	Q4_K_M	Quantized GGUF for llama.cpp etc.

Run with llama.cpp

llama-server -m viper.gguf --port 8080 -c 2048 -ngl 99

Then call the OpenAI-compatible endpoint:

curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"messages": [{"role": "user", "content": "Who developed you?"}]}'

Run with Ollama

ollama run hf.co/USERNAME/viper-ai

or create a local model:

ollama create viper -f ./Modelfile

Model details

Base architecture: Qwen2.5-7B-Instruct
Format: GGUF (Quantized) — Q4_K_M
Fine-tuned by: viperprojects47
System prompt: "You are Viper, a personal AI coding model fine-tuned for custom software development by viperprojects47."

Downloads last month: 8

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for viperprojects47/viper-ai

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Quantized

(395)

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