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"

Nex-N2.5-mini-APEX-I-MiniPlus-V1-GGUF / README.md

IsValorum

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	---
	base_model: nex-agi/Nex-N2.5-mini
	library_name: gguf
	tags:
	- gguf
	- apex
	- custom-quantization
	- unsloth-studio
	- moe
	- multimodal
	- vision
	- agentic
	- computer-use
	- llama.cpp
	- qwen35moe
	license: apache-2.0
	language:
	- en
	- zh
	pipeline_tag: image-text-to-text
	---

	# Nex-N2.5-mini APEX-I-MiniPlus (Multimodal Vision) GGUF
	### The Definitive 35B Agentic MoE Quantization · From 4GB Budget Laptops to 24GB Full 256K Context

	Welcome to APEX-I-MiniPlus for [nex-agi/Nex-N2.5-mini](https://huggingface.co/nex-agi/Nex-N2.5-mini) (Qwen3.5-MoE 35.1B multimodal agentic architecture).

	Most existing community quantizations are generated by automated bots that apply flat, blind bit-reduction across the entire model. This breaks sensitive layers, ruins vocabulary fidelity, and frequently omits the multimodal projector entirely.

	APEX-I-MiniPlus was engineered differently. This is a 100% custom, hand-crafted quantization created with surgically defined tensor-by-tensor rules, importance matrix calibration, and an included high-precision Q8_0 multimodal vision projector (`mmproj`).

	---

	## <a id="quick-navigation"></a>⚡ Quick Navigation Index
	- [📦 Model Files & Specifications](#model-specifications)
	- [🔬 Comparative Quantization Analysis (vs. Flat Quants & Generic APEX)](#comparative-analysis)
	- [👁️ Bundled Q8_0 High-Precision Vision Projector](#vision-projector)
	- [💻 Everyday Laptop Benchmarks (23–26+ tok/s on DDR4)](#laptop-benchmarks)
	- [🔥 The 24GB Miracle: Full 256K Context Runs In VRAM!](#context-scaling)
	- [🏎️ Hardware Throughput Projections (RTX 30 / 40 / 50)](#throughput-projections)
	- [🛠️ Handcrafted Layer Architecture](#tensor-map)
	- [📖 Recommended Configuration & Setup](#recommended-setup)

	---

	<a id="model-specifications"></a>
	## 📦 Model Files & Specifications

	\| File Name \| File Size \| Memory Footprint \| BPW \| Description \|
	\| :--- \| :--- \| :--- \| :--- \| :--- \|
	\| `Nex-N2.5-mini.APEX-I-MiniPlus.gguf` \| `14.56 GB` (`13.56 GiB`) \| `13.56 GiB` \| 3.36 BPW \| Main language, reasoning, tool-use & computer-use model \|
	\| `mmproj-nex-agi_Nex-N2.5-mini-Q8_0.gguf` \| `610 MB` (`582 MiB`) \| `582 MiB` \| 8.50 BPW \| Dedicated `Q8_0` vision projector for GUI parsing & high-res image input \|

	- Base Architecture: `qwen35moe` (35.1B parameters, multimodal agentic MoE).
	- Core Strengths: Autonomous computer-use, function calling, JSON schema compliance, high-resolution visual grounding.

	---

	<a id="comparative-analysis"></a>
	## 🔬 Comparative Quantization Analysis (vs. Flat Quants & Generic APEX)

	Also, don't confuse APEX-I-MiniPlus (Standard) with a generic baseline APEX-I-Mini. Traditional APEX-I-Mini drops core experts aggressively to 2-bit `IQ2_S` and leaves `output.weight` at 3-bit `Q3_K_M`, which creates a noticeable perplexity hit on complex reasoning tasks. Standard MiniPlus avoids that degradation floor while keeping boundary layers in linear `Q3_K` for single-cycle vectorized AVX2 CPU dequantization (hitting 23 to 26+ tok/s on DDR4 laptops), while protecting output in `Q6_K` and routers in `F32`.

	To put the numbers in perspective: this cuts nearly 2 GB off a flat 3-bit quant (approx. 15.6 GB), and weighs only about approx. 1 GB more than a generic APEX-I-Mini (approx. 12.5 GB). For that single extra gigabyte of VRAM, you get a massive jump in reasoning and syntactic stability while maximizing CPU/RAM execution throughput.

	Take a look at the tensor-by-tensor comparison table below to inspect the exact architectural differences and see why this specific allocation is optimal. That's specifically what this was built for:

	\| Architectural Component \| Generic Automated Quants (Flat `Q3_K_S` / `IQ3_S`) \| Generic APEX-I-Mini (Baseline Recipe) \| Our Handcrafted APEX-I-MiniPlus (Standard / IsValorum) \| Perceived Quality & Real-World Impact \|
	\| :--- \| :--- \| :--- \| :--- \| :--- \|
	\| Output Head (`output.weight`) \| Flat `IQ3_S` / `Q3_K_S` (approx. 3.44 BPW) \| Inherits base type `Q3_K_M` (approx. 3.44 BPW unarmored) \| `Q6_K` (approx. 6.56 BPW uncompromised) \| Eliminates Syntax & Vocabulary Hallucinations: Low-bit output heads cause tokenizer classification noise, breaking code indentation, brackets (`{}`, `[]`), math symbols, and domain terms. `Q6_K` preserves near-FP16 output classification. \|
	\| Expert Routers (`ffn_gate_inp.weight`) \| Blindly quantized to 3-bit / unoptimized \| Inherits base type `Q3_K_M` (approx. 3.44 BPW compressed) \| `F32` uncompressed (32.0 BPW, 2 MB/layer) \| Zero Router Drift: In micro-expert models, even minuscule quantization errors in router logits misdirect tokens to wrong experts. Retaining uncompressed `F32` guarantees 100% routing fidelity with virtually zero memory overhead (approx. 80 MB total). \|
	\| Attention & Language (`attn_output`, `attn_qkv`) \| Flat `IQ3_S` / `Q3_K_S` \| `Q3_K` on 34 middle layers (L3–36), `Q4_K` on 6 edge layers \| `Q6_K` for `attn_output`, `Q3_K` / `Q4_K` + `imatrix` \| Contextual Precision & CPU Throughput: Combines uncompromised `Q6_K` for the output projection with fast vectorized linear blocks for attention, balancing retrieval accuracy with maximum token streaming speed on CPU/RAM. \|
	\| Attention Gates (`attn_gate.weight`) \| Blindly compressed to 3-bit \| Compressed to `Q3_K` (middle) / `Q4_K` (edges) \| `Q4_K` / `Q8_0` (linear high-precision) \| Attention Routing Dynamics: High-precision linear gating modulating query-key projections without CPU dequantization latency. \|
	\| *Shared Foundation Expert (`ffn__shexp`) \| Flat `IQ3_S` / `Q3_K_S` (3.44 BPW) \| Linear `Q4_K` (middle) / `Q5_K` (edges) \| Linear `Q4_K` (middle) / `Q5_K` (edges) + `imatrix` \| Foundational Knowledge Stability:** Keeps the universal pathway in high-fidelity linear blocks, eliminating quantization drift while maintaining rapid single-cycle dequantization. \|
	\| Core MoE Layers (Middle: 10–29) \| Flat `IQ3_S` / `Q3_K_S` (uniform bit-rate across all layers) \| Aggressive `IQ2_S` (2.50 BPW) \| `IQ3_XXS` (3.06 BPW) + calibrated `imatrix` \| Above the Quality Threshold: Generic 2-bit `IQ2_S` baselines drop below the critical quality floor for 35B MoEs, resulting in perplexity spikes on reasoning tasks. Our `IQ3_XXS` with imatrix achieves deep compression (272 MiB → 98 MiB per block) without sacrificing logic. \|
	\| Edge MoE Layers (Layers 0–9 & 30–39) \| Flat `IQ3_S` / `Q3_K_S` (no layer-wise gradient) \| `Q3_K` (limited to first/last 5 layers only: L0–4, L35–39) \| `Q3_K` (expanded to 10 input & 10 output layers) \| AVX2 Single-Cycle Speed: Expanded 10+10 layer protection using linear `Q3_K` blocks enables single-cycle vectorized AVX2 CPU dequantization, unlocking 23 to 26+ tok/s on budget DDR4 laptops. \|
	\| Multimodal Vision (`mmproj`) \| Often omitted, or left as uncompressed `FP16` (approx. 900 MB) \| Often omitted or separate uncompressed `FP16` \| Bundled `Q8_0` (582 MB) with 27 critical F32/F16 fallbacks \| Saves approx. 320 MB VRAM with Zero Loss: Handcrafted quantization preserves normalization and bias tensors in F32/F16, ensuring razor-sharp OCR, DOM viewport reading, and coordinate detection without visual noise. \|
	\| Normalization & Biases \| Often degraded \| Standard \| `F32` uncompressed \| Numerical Stability: Prevents cumulative floating-point underflow/overflow across deep 40-layer computation. \|

	---

	<a id="vision-projector"></a>
	## 👁️ Bundled Q8_0 High-Precision Vision Projector

	Unlike text-only MoEs, Nex-N2.5-mini is designed for computer use, visual grounding, and multi-modal interaction.
	- Rather than leaving users to search for external FP16 projectors (approx. 900 MB), this repository bundles the official projector quantized to `Q8_0` (610 MB / 582 MiB).
	- Delivers near-lossless visual recognition while saving VRAM.

	---

	<a id="laptop-benchmarks"></a>
	## 💻 Everyday Laptop Benchmarks (23–26+ tok/s on DDR4)
	### Empirically Verified in Unsloth Studio

	- GPU VRAM Offload: Uses only 3.8 GB VRAM (fits effortlessly on budget 4GB and 6GB laptop GPUs like the RTX 4050, 3050, or older 1660 Ti/2060).
	- System Memory: Standard 32 GB DDR4 @ 3200 MHz holds the rest of the model.
	- Estimated Generation Speed: 23 to 26+ tokens/second sustained output!
	- Estimated Document Ingestion (Prefill): 300 to 410+ tokens/second.

	---

	<a id="context-scaling"></a>
	## 🔥 The 24GB Miracle: Full 256K Context Runs In VRAM!

	\| Context Length \| Model Weights (Est.) \| KV Cache (q8_0, 4 slots) \| Compute Buffers \| Total GPU VRAM (Est.) \| Hardware Verdict \|
	\| :--- \| :--- \| :--- \| :--- \| :--- \| :--- \|
	\| 32,512 (32k) \| `13.56 GiB` \| `0.57 GiB` \| `1.79 GiB` \| `15.92 GiB` \| Full offload on 24GB; 38/40 layers on 16GB \|
	\| 64,512 (64k) \| `13.56 GiB` \| `0.90 GiB` \| `1.93 GiB` \| `16.39 GiB` \| Effortless fit on 24GB GPUs \|
	\| 128,640 (128k) \| `13.56 GiB` \| `1.55 GiB` \| `2.20 GiB` \| `17.31 GiB` \| Effortless fit on 24GB GPUs \|
	\| 262,144 (Full 256K) \| `13.56 GiB` \| `2.90 GiB` \| `2.78 GiB` \| `19.24 GiB` \| 🔥 FULL 256K NATIVE CONTEXT IN VRAM! \|

	---

	<a id="throughput-projections"></a>
	## 🏎️ Hardware Throughput Projections (RTX 30 / 40 / 50)

	\| Hardware Target \| Offload Mode \| Generation Speed (Est.) \| Prompt Prefill Speed (Est.) \| Highlights \|
	\| :--- \| :--- \| :---: \| :---: \| : \|
	\| NVIDIA RTX 5080 / 5090 (Blackwell) \| Full GPU (`-ngl 99`) + mmproj \| 105 – 130+ tok/s \| 2,400 – 3,500+ tok/s \| Blistering agentic GUI interaction throughput \|
	\| NVIDIA RTX 4090 (24GB GDDR6X) \| Full GPU (`-ngl 99`) + mmproj \| 75 – 100+ tok/s \| 1,700 – 2,500+ tok/s \| Real-time computer-use screen analysis & tool calling \|
	\| NVIDIA RTX 3090 (24GB GDDR6) \| Full GPU (`-ngl 99`) + mmproj \| 62 – 78+ tok/s \| 1,350 – 1,950+ tok/s \| Full 256k multi-modal context in dedicated VRAM \|
	\| Consumer Laptop (4GB GPU + 32GB RAM)\| Hybrid Offload \| 20 – 24+ tok/s \| 300 – 420+ tok/s \| Smooth streaming from system DDR4/DDR5 RAM \|

	---

	<a id="tensor-map"></a>
	## 🛠️ Handcrafted Layer Architecture

	\| Component \| Target Layers \| Quant Type \| Rationale \|
	\| :--- \| :--- \| :--- \| :--- \|
	\| Output Head (`output.weight`) \| Final projection \| `Q6_K` \| Preserves probability distributions across 248k vocabulary tokens \|
	\| Token Embeddings \| Input projection \| `Q3_K` \| High semantic input fidelity \|
	\| Expert Routers (`ffn_gate_inp`) \| All layers (0–39) \| `F32` \| Uncompressed 32-bit floating point; 100% exact expert selection without routing noise \|
	\| Attention Output (`attn_output`) \| All layers \| `Q6_K` \| Uncompromised 6-bit attention projection across all layers \|
	\| Attention QKV & SSM States \| All layers \| `Q3_K / Q4_K` \| Fast vectorized AVX2 linear dequantization for tool-use responsiveness \|
	\| Core Routed Experts \| Layers 10 to 29 \| `IQ3_XXS` \| Maximum parameter compression (3.06 bpw) with importance matrix guidance \|
	\| Core Shared Experts \| Layers 10 to 29 \| `Q4_K` \| High-precision shared expert routing \|
	\| Edge Routed Experts \| Layers 0 to 9 & 30 to 39 \| `Q3_K` \| Protects prompt ingestion and response synthesis boundaries \|
	\| Edge Shared Experts \| Layers 0 to 9 & 30 to 39 \| `Q4_K` \| Armors foundational reasoning \|
	\| Normalization & Biases \| All layers \| `F32` \| Prevents cumulative floating point error \|
	\| Vision Projector (`mmproj`) \| Visual adapter \| `Q8_0` \| Ultra-high fidelity visual comprehension without FP16 bloat \|

	---

	<a id="recommended-setup"></a>
	## 📖 Recommended Configuration & Setup

	### Unsloth Studio:
	1. Load `Nex-N2.5-mini.APEX-I-MiniPlus.gguf`.
	2. Select `mmproj-nex-agi_Nex-N2.5-mini-Q8_0.gguf` as the vision projector.
	3. Configure KV Cache Dtype to `q8_0` and Context Checkpoints to `1`.
	4. Set GPU Offload to 100% (`-ngl 99`) on 24GB GPUs.

	### llama.cpp CLI:
	```bash
	llama-cli -m Nex-N2.5-mini.APEX-I-MiniPlus.gguf \
	--mmproj mmproj-nex-agi_Nex-N2.5-mini-Q8_0.gguf \
	-ngl 99 \
	-c 32768
	```