Instructions to use IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-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 IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-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 IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0

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 IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0

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 IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0

Use Docker

docker model run hf.co/IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-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": "IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-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/IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0

Ollama
How to use IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF with Ollama:
```
ollama run hf.co/IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0
```
Unsloth Desktop

How to use IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0

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": "IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF with Docker Model Runner:
```
docker model run hf.co/IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0
```

Lemonade

How to use IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0

Run and chat with the model

lemonade run user.Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF-Q8_0

List all available models

lemonade list

Hermes Agent

How to use IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-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 IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0

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 IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0

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 "IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF:Q8_0" \
  --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"

IsValorum commited on 14 days ago

Commit

7cac7fb

verified ·

1 Parent(s): 1cd0a4d

Clean duplicate banners, remove conflicting legacy text, and restore accurate Q8_0 specs

Browse files

Files changed (1) hide show

README.md +2 -13

README.md CHANGED Viewed

@@ -38,8 +38,8 @@ quantized_by: IsValorum
 > ### 🏛️ ARCHITECTURE SELECTION GUIDE — MINIPLUS V1 & V2.1 EDITIONS
 > This repository hosts the **MiniPlus V1** edition of **Nex-N2.5-mini**. Our releases are precision-engineered for specific hardware budgets and memory topologies. **V1 is NOT obsolete or inferior; it represents our leanest, most agile operating profile:**
 >
-> - **MiniPlus V1 (Lean & Agile Profile):** Highly compact footprint with uncompressed `F32` router gates, a fully armored `Q6_K` output head, and `IQ3_XXS` core experts. **Both V1 and V2.1 run flawlessly with the vast majority of the model residing in system RAM (DDR4/DDR5)**, thanks to linear CPU-friendly vectorization that avoids lookup stalls. V1 is dramatically superior to generic community APEX-I-Mini releases (which crush core reasoning down to 2-bit `IQ2_S`) and flat 3-bit quants.
-> - **MiniPlus V2.1 (Enhanced Protection & Refined Throughput):** Upgrades all 40 shared foundation experts to high-precision `Q5_K` and armors full attention anchor layers (`Q4_K`/`Q6_K`). This improves inference speed and stability even further for **only a few hundred megabytes extra** (~13.74 GiB vs ~13.56 GiB)—an overhead that is completely negligible when running in system RAM (16GB/32GB/64GB).
 >
 > 💡 **Which one should you choose?**
 > - **If your system has strict memory constraints:** **V1** delivers uncompromising reasoning at our lowest RAM footprint.
@@ -49,17 +49,6 @@ quantized_by: IsValorum
 > 👉 To explore or download the **V2.1** edition of Nex-N2.5-mini, visit:
 > **[IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V2.1-GGUF](https://huggingface.co/IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V2.1-GGUF)**
-# 🏛️ ARCHITECTURE SELECTION GUIDE — MINIPLUS TIER OVERVIEW
-> Every edition of the **MiniPlus** family is a precision-engineered, handcrafted quantization designed for specific hardware constraints and memory footprints. **None of these releases are obsolete; each represents an optimal operating point tailored to your system budget:**
->
-> - **MiniPlus V1 (Lean & Agile Foundation):** Maximum compactness and ultra-fast throughput with minimal RAM/VRAM footprint. Even in this lightest profile, **V1 dramatically outperforms generic community APEX-I-Mini releases** (which aggressively downgrade core reasoning to flat 2-bit `IQ2_S` and leave attention and output heads degraded). V1 provides uncompressed `F32` router gates, `Q6_K` output head protection, and `IQ3_XXS` core experts.
-> - **MiniPlus V2 (Expanded Edge Defense):** Adds wider protective envelopes on edge layers (10 layers in `IQ3_S` + `IQ4_NL` shared experts + `Q8_0` attention gates) for workstations with an extra ~1 GB of headroom seeking enhanced attention stability.
-> - **MiniPlus V2.1 (Current Long-Context Standard):** Upgrades shared experts to `Q5_K` across all 40 layers and optimizes full attention tensors (`Q4_K`/`Q6_K`) to eliminate AVX2 CPU dequantization stalls during deep offloading.
->
-> 💡 *Choose the version that fits your exact hardware budget! All editions provide rock-solid reasoning and far exceed generic community quants.*
-> 👉 If your workstation has sufficient memory headroom and you want the latest V2.1 specification, you can find it directly at:
-> **[IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V2.1-GGUF](https://huggingface.co/IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V2.1-GGUF)**
 ---
 ## <a id="quick-navigation"></a>⚡ Quick Navigation Index

 > ### 🏛️ ARCHITECTURE SELECTION GUIDE — MINIPLUS V1 & V2.1 EDITIONS
 > This repository hosts the **MiniPlus V1** edition of **Nex-N2.5-mini**. Our releases are precision-engineered for specific hardware budgets and memory topologies. **V1 is NOT obsolete or inferior; it represents our leanest, most agile operating profile:**
 >
+> - **MiniPlus V1 (Lean & Agile Profile):** Highly compact footprint with uncompressed `F32` router gates, a fully armored `Q6_K` output head, `Q8_0` attention gates, and `IQ3_XXS` core experts. **Both V1 and V2.1 run flawlessly with the vast majority of the model residing in system RAM (DDR4/DDR5)**, thanks to linear CPU-friendly vectorization that avoids lookup stalls. V1 is dramatically superior to generic community APEX-I-Mini releases (which crush core reasoning down to 2-bit `IQ2_S`) and flat 3-bit quants.
+> - **MiniPlus V2.1 (Enhanced Protection & Refined Throughput):** Upgrades all 40 shared foundation experts to high-precision `Q5_K` and armors full attention anchor layers (`Q4_K`/`Q6_K`). This improves inference speed and stability even further for **only ~180 MB more** (~13.74 GiB vs ~13.56 GiB)—an overhead that is completely negligible when running in system RAM (16GB/32GB/64GB).
 >
 > 💡 **Which one should you choose?**
 > - **If your system has strict memory constraints:** **V1** delivers uncompromising reasoning at our lowest RAM footprint.
 > 👉 To explore or download the **V2.1** edition of Nex-N2.5-mini, visit:
 > **[IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V2.1-GGUF](https://huggingface.co/IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V2.1-GGUF)**
 ---
 ## <a id="quick-navigation"></a>⚡ Quick Navigation Index