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

Remove third-party user references from model card

a0f439a verified 13 days ago

preview code

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metadata

base_model: nex-agi/Nex-N2.5-mini
library_name: gguf
tags:
  - quantization
  - quantized
  - gguf
  - apex
  - custom-quantization
  - unsloth-studio
  - moe
  - multimodal
  - vision
  - agentic
  - computer-use
  - llama.cpp
  - qwen35moe
license: apache-2.0
language:
  - en
  - zh
  - es
  - fr
  - de
  - pt
  - it
  - ru
  - ja
  - ko
  - vi
  - th
  - ar
pipeline_tag: image-text-to-text
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, 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 (System RAM Streaming Specialist with Deep Context): Specially prepared to run totally or partially in system RAM (DDR4/DDR5) across massive multimodal and agentic context windows (up to 256k tokens). Upgrades all 40 shared foundation experts to Q5_K, armors attention gates in Q8_0, and uses linear CPU-friendly vectorization that eliminates AVX2 lookup stalls. Depending on your processor and memory bandwidth (DDR4/DDR5), streaming generation in system RAM can be almost as fast as having everything in VRAM, while supporting deep context keeping the dedicated Q8_0 multimodal vision projector (mmproj) explicitly loaded in GPU VRAM for instant screen parsing and OCR. All for only approx. 180 MB more (approx. 13.74 GiB vs approx. 13.56 GiB)—an overhead that is completely negligible in system RAM.

Which one should you choose? (Official Recommendation: V2.1)

⭐ PRIMARY RECOMMENDATION — Nex-N2.5-mini APEX-I-MiniPlus V2.1: For virtually all users and deployments, V2.1 is the strictly recommended release. V2.1 reports a Perplexity of 6.4725 ± 0.1635 (ΔPPL ≈ +0.07 from unquantized baseline (approx. 6.40)), matching the token fidelity of Q5_K / Q6_K class quantizations while weighing only approx. 14.7 GB (same footprint as Q3_K_M). Furthermore, it eliminates AVX2 CPU stalls and is optimized for system RAM offload.

MiniPlus V1 Legacy: Maintained for architectural transparency and users seeking specialized configurations for their workflow.

Both editions are handcrafted and vastly outperform flat 3-bit quants and generic community APEX-I-Mini releases. 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

DO NOT CONFUSE APEX-I-MINIPLUS WITH GENERIC COMMUNITY APEX-I-MINI!

Regardless of release version (whether V1, V2, or V2.1), NEVER confuse handcrafted APEX-I-MiniPlus builds with generic community APEX-I-Mini releases:

Generic Community APEX-I-Mini: Uniformly compresses all core MoE experts down to aggressive 2-bit IQ2_S (dropping below the critical quality floor), leaves the sensitive token output head unarmored at 3-bit Q3_K_M, and compresses attention projections down to Q3_K. In deep reasoning models, this triggers severe perplexity spikes, syntax errors, and broken code brackets.

Handcrafted APEX-I-MiniPlus (All Editions by IsValorum): Every single MiniPlus release—from V1 and V2 to V2.1—is a custom tensor-by-tensor architecture that preserves uncompressed F32 router gates, armors the token output head in high-precision Q6_K, safeguards attention gates in Q8_0, and keeps core reasoning experts at or above calibrated 3-bit (IQ3_XXS/IQ3_S). Even our earlier builds vastly outperform generic community APEX recipes and flat 3-bit quants.

Quick Navigation Index

Model Files & Technical Specifications
Comparative Quantization Analysis (vs. Flat Quants & Generic APEX)
Bundled Q8_0 High-Precision Vision Projector
Everyday Laptop Guidance (DDR4 / DDR5 RAM)
The 24GB Miracle: Full 256K Context Runs In VRAM!
Hardware Throughput & Offload Benchmarks (RTX 30 / 40 / 50)
Recommended Generation Parameters (Creator Official)
Model Inherent Behavior vs. Quantization Fidelity Notice

Model Files & Specifications

File Name	File Size	Memory Footprint	BPW	Description
`Nex-N2.5-mini.APEX-I-MiniPlus-V1.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.

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 (optimized for DDR4/DDR5 laptop streaming), 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, supporting efficient streaming on budget DDR4/DDR5 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.

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.

Everyday Laptop Guidance

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.

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!

Hardware Throughput Projections (RTX 30 / 40 / 50)

⚙️ Recommended Generation Parameters (nex-agi Official)

Official sampling configuration recommended by nex-agi for optimal generation quality across coding, browser-use, and agent evaluations:

Hyperparameter	Value	Description / Creator Guidance
Temperature	`0.70`	Official nex-agi evaluation setting for coding (NexAU) and computer-use (NexCUA).
Top-P	`0.95`	Optimal balance between exploration and syntactic precision.
Top-K	`40`	Official top-k cutoff recommended in the model card for best generation quality.
Context Compaction	`Summary`	Creator recommends summary compaction when token usage exceeds 60% of context window.

🔍 Model Inherent Behavior vs. Quantization Fidelity Notice

Any behavioral nuances, stylistic tendencies, domain-specific habits, or zero-shot edge-case oversights stem entirely from the original unquantized checkpoint weights and fine-tuning distribution, NOT from the APEX-I quantization process. Handcrafted APEX-I-MiniPlus strictly preserves mathematical tensor fidelity—keeping 100% of expert routing matrices (gate_inp) in uncompressed F32 (zero router drift), armoring the token output head in Q6_K, and safeguarding attention gates in Q8_0. Empirical verification confirms near-zero perplexity loss (ΔPPL ≈ +0.07), ensuring that token logits, routing decisions, and reasoning trajectories are mathematically faithful to the original base model.