Instructions to use LibertAIDAI/Nex-N2-mini-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LibertAIDAI/Nex-N2-mini-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="LibertAIDAI/Nex-N2-mini-GGUF",
	filename="Nex-N2-mini-IQ4_XS.gguf",
)

llm.create_chat_completion(
	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"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use LibertAIDAI/Nex-N2-mini-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LibertAIDAI/Nex-N2-mini-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf LibertAIDAI/Nex-N2-mini-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LibertAIDAI/Nex-N2-mini-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf LibertAIDAI/Nex-N2-mini-GGUF: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 LibertAIDAI/Nex-N2-mini-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf LibertAIDAI/Nex-N2-mini-GGUF: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 LibertAIDAI/Nex-N2-mini-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LibertAIDAI/Nex-N2-mini-GGUF:Q4_K_M

Use Docker

docker model run hf.co/LibertAIDAI/Nex-N2-mini-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use LibertAIDAI/Nex-N2-mini-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LibertAIDAI/Nex-N2-mini-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": "LibertAIDAI/Nex-N2-mini-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/LibertAIDAI/Nex-N2-mini-GGUF:Q4_K_M

Ollama
How to use LibertAIDAI/Nex-N2-mini-GGUF with Ollama:
```
ollama run hf.co/LibertAIDAI/Nex-N2-mini-GGUF:Q4_K_M
```

Unsloth Studio

How to use LibertAIDAI/Nex-N2-mini-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 LibertAIDAI/Nex-N2-mini-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 LibertAIDAI/Nex-N2-mini-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use LibertAIDAI/Nex-N2-mini-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf LibertAIDAI/Nex-N2-mini-GGUF: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": "LibertAIDAI/Nex-N2-mini-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use LibertAIDAI/Nex-N2-mini-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf LibertAIDAI/Nex-N2-mini-GGUF: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 LibertAIDAI/Nex-N2-mini-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use LibertAIDAI/Nex-N2-mini-GGUF with Docker Model Runner:
```
docker model run hf.co/LibertAIDAI/Nex-N2-mini-GGUF:Q4_K_M
```

Lemonade

How to use LibertAIDAI/Nex-N2-mini-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LibertAIDAI/Nex-N2-mini-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Nex-N2-mini-GGUF-Q4_K_M

List all available models

lemonade list

Nex-N2-mini GGUF (imatrix, fixed chat template)

Imatrix-calibrated GGUF quantizations of nex-agi/Nex-N2-mini for llama.cpp — with a fixed chat template so reasoning extraction and tool calling work out of the box (see below).

Nex-N2-mini is a 35B-total / ~3B-active MoE (256 experts, 8 active) with hybrid linear attention, vision input, and "Agentic Thinking" adaptive reasoning. Apache 2.0.

Looking for Blackwell-optimized files? See LibertAIDAI/Nex-N2-mini-NVFP4-GGUF — NVFP4 expert tensors with native tensor-core kernels on RTX 50-series / B100/B200, faster batched serving than Q4_K_M on those GPUs.

Why these quants? Fixed chat template

The upstream chat template prefills the assistant turn with '<think>' (no trailing newline) while rendering past assistant reasoning as '<think>\n…'. This inconsistency breaks llama.cpp's reasoning parser: the forced-open think block is never recognized, so the full chain-of-thought (plus a stray </think>) leaks into content instead of reasoning_content — on every llama.cpp build, regardless of --reasoning-format. Community GGUFs that embed the upstream template inherit this bug.

These files embed a corrected template (one added newline). With stock llama-server --jinja:

reasoning_content / content are separated correctly,
tool calls parse into structured tool_calls,
no extra flags needed.

All quants below (except Q8_0, which doesn't use it) were quantized with an importance matrix computed from the BF16 weights over a diverse ~64k-token calibration set (the imatrix file is included in this repo).

About LibertAI

LibertAI is a decentralized AI platform — private inference, an OpenAI-compatible API, and a chat UI, all running on community GPUs over Aleph Cloud instead of a single company's servers. No accounts required to chat, no logs sent home, and the same models you'd self-host are available behind a sovereign endpoint.

If you want to put this model (or any other) to work as an autonomous agent without running your own infrastructure, check out LiberClaw — Hermes-style agents hosted on Aleph Cloud with LibertAI inference. Free tier: 2 agents, no credit card, 5 minutes to deploy. Open source.

Files

File	Size	When to pick
`Nex-N2-mini-IQ4_XS.gguf`	18.7 GB	Smallest — fits a 24 GB GPU with long context
`Nex-N2-mini-Q4_K_M.gguf`	21.2 GB	Recommended — best size/quality balance
`Nex-N2-mini-Q5_K_M.gguf`	24.7 GB	Higher quality, still fits 32 GB GPUs
`Nex-N2-mini-Q6_K.gguf`	28.5 GB	Near-lossless
`Nex-N2-mini-Q8_0.gguf`	36.9 GB	Highest quality (needs >32 GB VRAM or partial offload)
`mmproj-Nex-N2-mini-F16.gguf`	903 MB	Required for image input — works with all of the above
`Nex-N2-mini.imatrix`	192 MB	The importance matrix used (for making your own quants)

Usage

Text-only (CLI)

llama-cli -m Nex-N2-mini-Q4_K_M.gguf -ngl 999 -c 8192 -p "Your prompt here"

Multimodal (server, vision + text)

llama-server \
  -m Nex-N2-mini-Q4_K_M.gguf \
  --mmproj mmproj-Nex-N2-mini-F16.gguf \
  -ngl 999 -c 32768 --jinja \
  --host 0.0.0.0 --port 8080

Then POST to /v1/chat/completions — reasoning arrives in reasoning_content, answers in content, tool calls in tool_calls. To disable thinking, set chat_template_kwargs: {"enable_thinking": false} in the request.

About the architecture

Nex-N2-mini is built on the Qwen3.5-MoE architecture (qwen35moe in GGUF): 40 layers, 3 of every 4 using linear attention with every 4th full attention, 256 routed experts (8 active) plus a shared expert. The upstream config declares a 1-layer MTP head, but the published checkpoints do not include MTP weights, so no MTP/speculative variant can be produced from public weights.

Sources & credits

Base model: nex-agi/Nex-N2-mini by Nex AGI — Apache 2.0
Calibration data for the imatrix: bartowski's calibration_datav3
Tooling: llama.cpp convert_hf_to_gguf.py, llama-imatrix, llama-quantize

License

Apache 2.0, inherited from the upstream model.

Downloads last month: 442

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

Model tree for LibertAIDAI/Nex-N2-mini-GGUF

Base model

nex-agi/Nex-N2-mini

Quantized

(36)

this model