Instructions to use abenzerps/Nex-N2.5-mini-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 abenzerps/Nex-N2.5-mini-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 abenzerps/Nex-N2.5-mini-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf abenzerps/Nex-N2.5-mini-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf abenzerps/Nex-N2.5-mini-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf abenzerps/Nex-N2.5-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 abenzerps/Nex-N2.5-mini-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf abenzerps/Nex-N2.5-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 abenzerps/Nex-N2.5-mini-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf abenzerps/Nex-N2.5-mini-GGUF:Q4_K_M

Use Docker

docker model run hf.co/abenzerps/Nex-N2.5-mini-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use abenzerps/Nex-N2.5-mini-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "abenzerps/Nex-N2.5-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": "abenzerps/Nex-N2.5-mini-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/abenzerps/Nex-N2.5-mini-GGUF:Q4_K_M

Ollama
How to use abenzerps/Nex-N2.5-mini-GGUF with Ollama:
```
ollama run hf.co/abenzerps/Nex-N2.5-mini-GGUF:Q4_K_M
```
Unsloth Desktop

How to use abenzerps/Nex-N2.5-mini-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf abenzerps/Nex-N2.5-mini-GGUF:Q4_K_M

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": "abenzerps/Nex-N2.5-mini-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use abenzerps/Nex-N2.5-mini-GGUF with Docker Model Runner:
```
docker model run hf.co/abenzerps/Nex-N2.5-mini-GGUF:Q4_K_M
```

Lemonade

How to use abenzerps/Nex-N2.5-mini-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull abenzerps/Nex-N2.5-mini-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Nex-N2.5-mini-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use abenzerps/Nex-N2.5-mini-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 abenzerps/Nex-N2.5-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 abenzerps/Nex-N2.5-mini-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use abenzerps/Nex-N2.5-mini-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf abenzerps/Nex-N2.5-mini-GGUF:Q4_K_M

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 "abenzerps/Nex-N2.5-mini-GGUF:Q4_K_M" \
  --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"

Nex-N2.5-mini GGUF

GGUF files for Nex-N2.5-mini, a long-context agentic model for coding, tool use, computer use, and multimodal workloads. The source checkpoint supports a native context length of 262,144 tokens (256K).

Benchmarks

Benchmark results reported by Nex-AGI for the original Nex-N2.5 family. These figures are not measurements of this GGUF conversion.

GGUF files

Quantization	File	Size (GB)
Q8_0	Nex-N2.5-mini-Q8_0.gguf	36.90 GB
Q6_K	Nex-N2.5-mini-Q6_K.gguf	28.51 GB
Q5_K_M	Nex-N2.5-mini-Q5_K_M.gguf	24.73 GB
Q5_K_S	Nex-N2.5-mini-Q5_K_S.gguf	23.98 GB
Q4_K_M	Nex-N2.5-mini-Q4_K_M.gguf	21.17 GB
Q4_K_S	Nex-N2.5-mini-Q4_K_S.gguf	19.89 GB
Q4_0	Nex-N2.5-mini-Q4_0.gguf	19.72 GB
Q3_K_M	Nex-N2.5-mini-Q3_K_M.gguf	16.76 GB
Q3_K_S	Nex-N2.5-mini-Q3_K_S.gguf	15.18 GB
IQ3_XXS	Nex-N2.5-mini-IQ3_XXS.gguf	13.62 GB
Q2_K	Nex-N2.5-mini-Q2_K.gguf	12.94 GB
IQ2_M	Nex-N2.5-mini-IQ2_M.gguf	11.66 GB
IQ2_XS	Nex-N2.5-mini-IQ2_XS.gguf	10.51 GB
IQ2_XXS	Nex-N2.5-mini-IQ2_XXS.gguf	9.50 GB
IQ1_M	Nex-N2.5-mini-IQ1_M.gguf	8.24 GB
TQ1_0	Nex-N2.5-mini-TQ1_0.gguf	7.90 GB
IQ1_S	Nex-N2.5-mini-IQ1_S.gguf	7.48 GB

TQ1_0 is an experimental ternary quantization. IQ1_S and IQ1_M use importance-matrix quantization.

Multimodal projector

File	Size	Description
mmproj-Nex-N2.5-mini-F16.gguf	899 MB	F16 vision projector for runtimes with multimodal support

The projector is optional for text-only use. Use it with a current llama.cpp build that supports the model's multimodal path.

Chat template

The GGUF embeds the upstream chat template. An external copy is provided as chat_template.jinja for runtimes that require a separate template file.

Usage

For text generation with llama.cpp:

llama-cli \
  -m Nex-N2.5-mini-Q4_K_M.gguf \
  -c 8192 --jinja \
  --temp 0.7 --top-p 0.95 \
  -p "Explain why reproducible builds matter."

For an OpenAI-compatible server:

llama-server \
  -m Nex-N2.5-mini-Q4_K_M.gguf \
  -c 8192 --jinja --host 0.0.0.0 --port 8080

Increase -c up to 262144 when sufficient memory is available. Tool-call behavior depends on the serving runtime and its parser integration; use the embedded template and verify tool calls in the target application.

Source and build

Source model: nex-agi/Nex-N2.5-mini
Source revision: 87420286149d9cce9bd46cd335ef9bda33c37c1b
Conversion: ggml-org/llama.cpp commit f3f1a8f2760f28325a5ec20c05b171e5b7c83a29
License: Apache-2.0
Checksums: SHA256SUMS.txt

Downloads last month: 11,531

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

1-bit

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Model tree for abenzerps/Nex-N2.5-mini-GGUF

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Collection including abenzerps/Nex-N2.5-mini-GGUF

Nex-N2.5

Collection

Nex-N2.5 model family releases, including GGUF and MLX conversions. • 4 items • Updated 2 days ago