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 11 days ago

Commit

e2150ee

verified ·

1 Parent(s): a2869d9

Clarify external benchmark attribution

Browse files

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README.md +189 -189

README.md CHANGED Viewed

@@ -1,189 +1,189 @@
----
-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
----
-> [!NOTE]
-> ### 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 (+24 to 28+ tok/s). 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](https://huggingface.co/IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V2.1-GGUF):** For virtually all users and deployments, **V2.1 is the strictly recommended release**. Empirically verified on WikiText-2, V2.1 achieves an outstanding **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 completely eliminates AVX2 CPU stalls, providing blistering **+24 to 28+ tok/s streaming** under 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](https://huggingface.co/IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V2.1-GGUF)**
-> [!WARNING]
-> ### 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.
----
-## <a id="quick-navigation"></a>Quick Navigation Index
-- [Model Files & Technical Specifications](#model-specifications)
-- [Comparative Quantization Analysis (vs. Flat Quants & Generic APEX)](#comparative-analysis)
-- [Independent Benchmark of the APEX-I-MiniPlus Family (Occamy V2 Reference)](#independent-benchmark)
-- [Bundled Q8_0 High-Precision Vision Projector](#vision-projector)
-- [Everyday Laptop Benchmarks (DDR4 / DDR5 RAM)](#laptop-benchmarks)
-- [The 24GB Miracle: Full 256K Context Runs In VRAM!](#context-scaling)
-- [Hardware Throughput & Offload Benchmarks (RTX 30 / 40 / 50)](#throughput-projections)
-- [Recommended Generation Parameters (Creator Official)](#generation-parameters)
-- [Model Inherent Behavior vs. Quantization Fidelity Notice](#quantization-fidelity)
----
-<a id="independent-benchmark"></a>
-### 🏅 Independent Benchmark of the APEX-I-MiniPlus Family (Occamy V2 Reference)
-> [!NOTE]
-> **Occamy V2 Reference Notice:** The benchmark below was performed on **Occamy-1.0 APEX-I-MiniPlus V2**, not on this specific Nex-N2.5 model. It is included as independent evidence of the broader APEX-I-MiniPlus quantization approach and hybrid MoE architecture.
-The **APEX-I-MiniPlus** quantization architecture powering this model was subjected to an extensive independent evaluation by Japanese AI researcher and evaluator [zephel01 (CoolZero)](https://note.com/zephel01/n/n71d3d7e6b70c?hl=en) on an **NVIDIA RTX 5090 (32GB)** workstation running `llama.cpp` CUDA `b11027` with FlashAttention (`-fa on -ctk q8_0 -ctv q8_0 -ngl 99`).
-The evaluation tested the APEX-I hybrid MoE engine across **348 unseeded trials** on SWE-bench style multi-file Python bug-fixing tasks with hidden `pytest` suites (`llmbench`):
-- **L6 Multi-File Code Generation (60 tasks):**
-  - **Context 32,768 (32K):** **93.3% Resolved** (46/60 tasks passed 5/5 consecutive trials; 20/20 on Easy–Hard).
-  - **Context 65,536 (65K):** **90.0% Resolved** (45/60 tasks passed 5/5 consecutive trials).
-  - **Match with 25–28 GB Models:** Matches or exceeds the resolution rate of full 25–28 GB models (such as `Ornith-1.5` and `Tiel-Coder` 35B-A3B) while consuming **over 10 GB less VRAM** (14.6 GB vs approx. 26 GB).
-- **Extreme Context VRAM Scaling (The Hybrid DeltaNet SSM Advantage):**
-  - **32K Context:** **14.6 GB** total VRAM allocation.
-  - **65K Context:** **15.1 GB** total VRAM allocation (only **+0.5 GB VRAM** added when doubling context!).
-  - *Architectural Explanation:* Because 30 of the 40 layers utilize Linear Attention / DeltaNet SSM ($O(1)$ constant recurrence memory), only the 10 full-attention anchor layers expand the KV cache. This proves empirically that **65,536 context runs 100% in VRAM on consumer 16GB GPUs (RTX 4080 / RTX 5080)** without offloading to system RAM.
-- **Measured Real-World Throughput:** Sustained single-stream generation of **approx. 247 – 251 tok/s** on NVIDIA RTX 5090.
----
-<a id="model-specifications"></a>
-## 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.
----
-<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`) | **approx. 247 – 251 tok/s** | **2,800 – 3,900+ tok/s** | Empirically verified on RTX 5090 by zephel01 (Occamy V2 Reference) |
-| **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="generation-parameters"></a>
-### ⚙️ Recommended Generation Parameters (nex-agi Official)
-Official sampling configuration recommended by [nex-agi](https://huggingface.co/nex-agi/Nex-N2.5-mini) 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. |
-> [!IMPORTANT]
-> <a id="quantization-fidelity"></a>
-> ### 🔍 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.

+---
+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
+---
+> [!NOTE]
+> ### 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 (+24 to 28+ tok/s). 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](https://huggingface.co/IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V2.1-GGUF):** For virtually all users and deployments, **V2.1 is the strictly recommended release**. Empirically verified on WikiText-2, V2.1 achieves an outstanding **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 completely eliminates AVX2 CPU stalls, providing blistering **+24 to 28+ tok/s streaming** under 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](https://huggingface.co/IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V2.1-GGUF)**
+> [!WARNING]
+> ### 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.
+---
+## <a id="quick-navigation"></a>Quick Navigation Index
+- [Model Files & Technical Specifications](#model-specifications)
+- [Comparative Quantization Analysis (vs. Flat Quants & Generic APEX)](#comparative-analysis)
+- [Independent Benchmark of the APEX-I-MiniPlus Family (Occamy V2 Reference)](#independent-benchmark)
+- [Bundled Q8_0 High-Precision Vision Projector](#vision-projector)
+- [Everyday Laptop Benchmarks (DDR4 / DDR5 RAM)](#laptop-benchmarks)
+- [The 24GB Miracle: Full 256K Context Runs In VRAM!](#context-scaling)
+- [Hardware Throughput & Offload Benchmarks (RTX 30 / 40 / 50)](#throughput-projections)
+- [Recommended Generation Parameters (Creator Official)](#generation-parameters)
+- [Model Inherent Behavior vs. Quantization Fidelity Notice](#quantization-fidelity)
+---
+<a id="independent-benchmark"></a>
+### 🏅 Independent Benchmark of the APEX-I-MiniPlus Family (Occamy V2 Reference)
+> [!NOTE]
+> **Occamy V2 Reference Notice:** **External report:** [zephel01 independently benchmarked Occamy V2](https://note.com/zephel01/n/n71d3d7e6b70c?hl=en). The benchmark below was performed on **Occamy-1.0 APEX-I-MiniPlus V2**, not on this specific Nex-N2.5 model. It is included as independent evidence of the broader APEX-I-MiniPlus quantization approach and hybrid MoE architecture.
+The **APEX-I-MiniPlus** quantization architecture powering this model was subjected to an extensive independent evaluation by Japanese AI researcher and evaluator [zephel01 (CoolZero)](https://note.com/zephel01/n/n71d3d7e6b70c?hl=en) on an **NVIDIA RTX 5090 (32GB)** workstation running `llama.cpp` CUDA `b11027` with FlashAttention (`-fa on -ctk q8_0 -ctv q8_0 -ngl 99`).
+The evaluation tested the APEX-I hybrid MoE engine across **348 unseeded trials** on SWE-bench style multi-file Python bug-fixing tasks with hidden `pytest` suites (`llmbench`):
+- **L6 Multi-File Code Generation (60 tasks):**
+  - **Context 32,768 (32K):** **93.3% Resolved** (46/60 tasks passed 5/5 consecutive trials; 20/20 on Easy–Hard).
+  - **Context 65,536 (65K):** **90.0% Resolved** (45/60 tasks passed 5/5 consecutive trials).
+  - **Match with 25–28 GB Models:** Matches or exceeds the resolution rate of full 25–28 GB models (such as `Ornith-1.5` and `Tiel-Coder` 35B-A3B) while consuming **over 10 GB less VRAM** (14.6 GB vs approx. 26 GB).
+- **Extreme Context VRAM Scaling (The Hybrid DeltaNet SSM Advantage):**
+  - **32K Context:** **14.6 GB** total VRAM allocation.
+  - **65K Context:** **15.1 GB** total VRAM allocation (only **+0.5 GB VRAM** added when doubling context!).
+  - *Architectural Explanation:* Because 30 of the 40 layers utilize Linear Attention / DeltaNet SSM ($O(1)$ constant recurrence memory), only the 10 full-attention anchor layers expand the KV cache. This proves empirically that **65,536 context runs 100% in VRAM on consumer 16GB GPUs (RTX 4080 / RTX 5080)** without offloading to system RAM.
+- **Measured Real-World Throughput:** Sustained single-stream generation of **approx. 247 – 251 tok/s** on NVIDIA RTX 5090.
+---
+<a id="model-specifications"></a>
+## 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.
+---
+<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`) | **approx. 247 – 251 tok/s** | **2,800 – 3,900+ tok/s** | Empirically verified on RTX 5090 by zephel01 (Occamy V2 Reference) |
+| **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="generation-parameters"></a>
+### ⚙️ Recommended Generation Parameters (nex-agi Official)
+Official sampling configuration recommended by [nex-agi](https://huggingface.co/nex-agi/Nex-N2.5-mini) 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. |
+> [!IMPORTANT]
+> <a id="quantization-fidelity"></a>
+> ### 🔍 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.