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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sjakek/Nex-N2-mini-GGUF",
	filename="Nex-N2-mini-UD-Q3_K_XL.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use sjakek/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 sjakek/Nex-N2-mini-GGUF:UD-Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf sjakek/Nex-N2-mini-GGUF:UD-Q4_K_M

Install from WinGet (Windows)

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

Use Docker

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

LM Studio
Jan

vLLM

How to use sjakek/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 "sjakek/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": "sjakek/Nex-N2-mini-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

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

Unsloth Studio

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

How to use sjakek/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 sjakek/Nex-N2-mini-GGUF:UD-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": "sjakek/Nex-N2-mini-GGUF:UD-Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use sjakek/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 sjakek/Nex-N2-mini-GGUF:UD-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 sjakek/Nex-N2-mini-GGUF:UD-Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use sjakek/Nex-N2-mini-GGUF with Docker Model Runner:
```
docker model run hf.co/sjakek/Nex-N2-mini-GGUF:UD-Q4_K_M
```

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Nex-N2-mini GGUF

GGUF quantizations of nex-agi/Nex-N2-mini for use with llama.cpp.

These are Unsloth-style UD (dynamic) quants: per-tensor quantization types tuned with an importance matrix, using the same recipe family as Unsloth’s Qwen3.6 35B-A3B MoE GGUF releases.

Model at a glance


Architecture	`qwen35moe` (Qwen3.5 / 3.6 MoE family)
Trunk layers	40
Experts	256 total, 8 active per token
Context (train)	262144 tokens
Vocab	248320
Vision	Supported via `mmproj-BF16.gguf` (optional)
MTP draft head	Not included in this release (see note below)

Files

File	Size	When to use
`Nex-N2-mini-UD-Q3_K_XL.gguf`	~17 GB	Smallest; more VRAM-friendly
`Nex-N2-mini-UD-Q4_K_M.gguf`	~22 GB	Good default balance
`Nex-N2-mini-UD-Q4_K_XL.gguf`	~22 GB	Recommended quality / size sweet spot
`Nex-N2-mini-UD-Q5_K_XL.gguf`	~27 GB	Higher quality
`Nex-N2-mini-UD-Q6_K_XL.gguf`	~32 GB	Highest quality in this set
`mmproj-BF16.gguf`	~0.9 GB	Image / vision input (optional)
`imatrix_unsloth.gguf_file`	~0.2 GB	Importance matrix used during quantization (reference only)

All .gguf model files are at the repo root (flat layout).

Quick start

Chat server (recommended)

llama-server \
  -m Nex-N2-mini-UD-Q4_K_XL.gguf \
  --host 127.0.0.1 --port 8080 \
  -c 8192 \
  -ngl 99 \
  -fa on \
  --cache-type-k q8_0 \
  --cache-type-v q8_0

Open http://127.0.0.1:8080 in your browser for the built-in chat UI.

CLI

llama-cli -m Nex-N2-mini-UD-Q4_K_XL.gguf -ngl 99 -fa on

Vision (optional)

Add the projector when you need image input:

llama-server \
  -m Nex-N2-mini-UD-Q4_K_XL.gguf \
  --mmproj mmproj-BF16.gguf \
  -ngl 99 \
  -fa on

Text-only chat works fine without --mmproj.

VRAM and MoE offloading

This is a Mixture-of-Experts model. Even quantized, full GPU residency may not fit on smaller cards.

-ngl 99 (or --gpu-layers 99): offloads attention and dense weights to the GPU.
-ncmoe N / --n-cpu-moe N: keeps routed expert weights for the first N layers in system RAM; later layers stay on GPU. Useful on 12–16 GB GPUs.

Example for a ~12 GB GPU (adjust N to taste):

llama-server -m Nex-N2-mini-UD-Q4_K_XL.gguf -ngl 99 -ncmoe 30 -fa on -c 8192 \
  --cache-type-k q8_0 --cache-type-v q8_0

Higher -ncmoe = more expert layers on CPU = lower VRAM use, slower generation.

MTP note

The upstream Nex config mentions an MTP (multi-token prediction) block, but these GGUF files contain 40 trunk layers only — no MTP draft weights are present in the published checkpoint.

Files in this repo use GGUF metadata block_count=40 and nextn_predict_layers=0, so they load cleanly in current llama.cpp without extra flags.

If you have an older copy of these files that fails with missing tensor blk.40.attn_norm.weight, re-download from this repo, or add:

--override-kv qwen35moe.block_count=int:40,qwen35moe.nextn_predict_layers=int:0

How these quants were built

Source model: nex-agi/Nex-N2-mini
Importance matrix: from unsloth/Qwen3.6-35B-A3B-GGUF (imatrix_unsloth.gguf_file)
Tensor-type recipes: Unsloth Qwen3.6 UD layouts (same family as Qwen3.5/3.6 35B-A3B MoE)
Tooling: llama.cpp convert_hf_to_gguf.py + llama-quantize

License

Apache 2.0 (see upstream Nex-N2-mini for model terms).

Downloads last month: 129

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

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

Base model

nex-agi/Nex-N2-mini

Quantized

(21)

this model