Instructions to use tarruda/Nex-N2-Pro-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 tarruda/Nex-N2-Pro-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 tarruda/Nex-N2-Pro-GGUF:IQ3_XXS
# Run inference directly in the terminal:
llama cli -hf tarruda/Nex-N2-Pro-GGUF:IQ3_XXS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tarruda/Nex-N2-Pro-GGUF:IQ3_XXS
# Run inference directly in the terminal:
llama cli -hf tarruda/Nex-N2-Pro-GGUF:IQ3_XXS

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 tarruda/Nex-N2-Pro-GGUF:IQ3_XXS
# Run inference directly in the terminal:
./llama-cli -hf tarruda/Nex-N2-Pro-GGUF:IQ3_XXS

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 tarruda/Nex-N2-Pro-GGUF:IQ3_XXS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tarruda/Nex-N2-Pro-GGUF:IQ3_XXS

Use Docker

docker model run hf.co/tarruda/Nex-N2-Pro-GGUF:IQ3_XXS

LM Studio
Jan

vLLM

How to use tarruda/Nex-N2-Pro-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tarruda/Nex-N2-Pro-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": "tarruda/Nex-N2-Pro-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/tarruda/Nex-N2-Pro-GGUF:IQ3_XXS

Ollama
How to use tarruda/Nex-N2-Pro-GGUF with Ollama:
```
ollama run hf.co/tarruda/Nex-N2-Pro-GGUF:IQ3_XXS
```

Unsloth Studio

How to use tarruda/Nex-N2-Pro-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 tarruda/Nex-N2-Pro-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 tarruda/Nex-N2-Pro-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tarruda/Nex-N2-Pro-GGUF to start chatting

How to use tarruda/Nex-N2-Pro-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tarruda/Nex-N2-Pro-GGUF:IQ3_XXS

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": "tarruda/Nex-N2-Pro-GGUF:IQ3_XXS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use tarruda/Nex-N2-Pro-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tarruda/Nex-N2-Pro-GGUF:IQ3_XXS

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 "tarruda/Nex-N2-Pro-GGUF:IQ3_XXS" \
  --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 tarruda/Nex-N2-Pro-GGUF with Docker Model Runner:
```
docker model run hf.co/tarruda/Nex-N2-Pro-GGUF:IQ3_XXS
```

Lemonade

How to use tarruda/Nex-N2-Pro-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tarruda/Nex-N2-Pro-GGUF:IQ3_XXS

Run and chat with the model

lemonade run user.Nex-N2-Pro-GGUF-IQ3_XXS

List all available models

lemonade list

Hermes Agent

How to use tarruda/Nex-N2-Pro-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 tarruda/Nex-N2-Pro-GGUF:IQ3_XXS

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 tarruda/Nex-N2-Pro-GGUF:IQ3_XXS

Run Hermes

hermes

Atomic Chat

Are you actively using this?

by qenme - opened 12 days ago

Discussion

qenme

12 days ago

I'm curious how you liked the model? Ornith seemed to be a bust, and Q3.5 397b is too old and never got updated. Did this quant work well for you?

tarruda

Owner 12 days ago

I'm not actively using because qwen 3.5 122b and deepseek v4 flash both the best intelligence/efficiency ratio for my hardware, but I like this model a lot.

I have a few private benchmarks that only this model can solve consistently across all local models I was able to run locally. There's something special about its caveman reasoning that makes it stand out, but one downside is that it thinks a lot and every once in a while gets stuck in a reasoning loop.

IDK if the infinite reasoning loop is due to quantization, but I tried nex n2 mini without quantization (bf16) and happened there too. I wish they'd build a version of this model on top of qwen 3.5 122B.

qenme

12 days ago

I'm not actively using because qwen 3.5 122b and deepseek v4 flash both the best intelligence/efficiency ratio for my hardware, but I like this model a lot.

I have a few private benchmarks that only this model can solve consistently across all local models I was able to run locally. There's something special about its caveman reasoning that makes it stand out, but one downside is that it thinks a lot and every once in a while gets stuck in a reasoning loop.

IDK if the infinite reasoning loop is due to quantization, but I tried nex n2 mini without quantization (bf16) and happened there too. I wish they'd build a version of this model on top of qwen 3.5 122B.

I see. Thanks for the perspective and quant!

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