Instructions to use IsValorum/XYZ-Aquila-mini-APEX-I-MiniPlus-V2-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/XYZ-Aquila-mini-APEX-I-MiniPlus-V2-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/XYZ-Aquila-mini-APEX-I-MiniPlus-V2-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf IsValorum/XYZ-Aquila-mini-APEX-I-MiniPlus-V2-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/XYZ-Aquila-mini-APEX-I-MiniPlus-V2-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf IsValorum/XYZ-Aquila-mini-APEX-I-MiniPlus-V2-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/XYZ-Aquila-mini-APEX-I-MiniPlus-V2-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf IsValorum/XYZ-Aquila-mini-APEX-I-MiniPlus-V2-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/XYZ-Aquila-mini-APEX-I-MiniPlus-V2-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf IsValorum/XYZ-Aquila-mini-APEX-I-MiniPlus-V2-GGUF:Q8_0

Use Docker

docker model run hf.co/IsValorum/XYZ-Aquila-mini-APEX-I-MiniPlus-V2-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use IsValorum/XYZ-Aquila-mini-APEX-I-MiniPlus-V2-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IsValorum/XYZ-Aquila-mini-APEX-I-MiniPlus-V2-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/XYZ-Aquila-mini-APEX-I-MiniPlus-V2-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/XYZ-Aquila-mini-APEX-I-MiniPlus-V2-GGUF:Q8_0

Ollama
How to use IsValorum/XYZ-Aquila-mini-APEX-I-MiniPlus-V2-GGUF with Ollama:
```
ollama run hf.co/IsValorum/XYZ-Aquila-mini-APEX-I-MiniPlus-V2-GGUF:Q8_0
```
Unsloth Desktop

How to use IsValorum/XYZ-Aquila-mini-APEX-I-MiniPlus-V2-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/XYZ-Aquila-mini-APEX-I-MiniPlus-V2-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/XYZ-Aquila-mini-APEX-I-MiniPlus-V2-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use IsValorum/XYZ-Aquila-mini-APEX-I-MiniPlus-V2-GGUF with Docker Model Runner:
```
docker model run hf.co/IsValorum/XYZ-Aquila-mini-APEX-I-MiniPlus-V2-GGUF:Q8_0
```

Lemonade

How to use IsValorum/XYZ-Aquila-mini-APEX-I-MiniPlus-V2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull IsValorum/XYZ-Aquila-mini-APEX-I-MiniPlus-V2-GGUF:Q8_0

Run and chat with the model

lemonade run user.XYZ-Aquila-mini-APEX-I-MiniPlus-V2-GGUF-Q8_0

List all available models

lemonade list

Hermes Agent

How to use IsValorum/XYZ-Aquila-mini-APEX-I-MiniPlus-V2-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/XYZ-Aquila-mini-APEX-I-MiniPlus-V2-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/XYZ-Aquila-mini-APEX-I-MiniPlus-V2-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use IsValorum/XYZ-Aquila-mini-APEX-I-MiniPlus-V2-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/XYZ-Aquila-mini-APEX-I-MiniPlus-V2-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/XYZ-Aquila-mini-APEX-I-MiniPlus-V2-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"

XYZ-Aquila-mini APEX-I-MiniPlus-V2 (Multimodal Vision) GGUF

The Definitive 35B Multimodal Search & UI Agent MoE · Bundled Q8_0 Vision Projector · From 4GB Laptops to 24GB Full 256K Context

Welcome to APEX-I-MiniPlus-V2 for XYZAILab/XYZ-Aquila-mini (Qwen3.5-MoE 35B multimodal architecture engineered by XYZ AI Lab specifically for agentic web search, complex UI grounding, browser interaction, and visual reasoning).

Most community quantizations apply crude automated bit-reduction across the model, corrupting visual grounding coordinates, degrading web query formulation, and often leaving users without a functional vision projector.

APEX-I-MiniPlus-V2 was engineered differently. This is a 100% custom, hand-crafted quantization built with surgical tensor-by-tensor overrides, calibrated importance matrices (imatrix), and an included high-precision Q8_0 multimodal vision projector (mmproj). Whether executing live web search workflows on an everyday laptop or orchestrating autonomous browser agents on a 24GB GPU, this release delivers an unprecedented combination of visual precision, reasoning throughput, and memory efficiency.

⚡ Quick Navigation Index

📦 Bundled Model Files & Specifications
🔬 Comparative Quantization Analysis (vs. Flat Quants & Generic APEX)
👁️ Bundled Q8_0 High-Precision Multimodal Vision Projector
💻 Everyday Laptop Benchmarks (DDR4 / DDR5 RAM)
🔥 The 24GB Miracle: Full 256K Context Runs In VRAM!
🏎️ Hardware Throughput Projections (RTX 30 / 40 / 50)
⚖️ The Speed vs. Precision Trade-off
🛠️ Surgical Tensor Quantization Map
📖 Recommended Configuration & Setup

📦 Bundled Model Files & Specifications

File Name	File Size	Memory Footprint	Format / Precision	Purpose
`XYZ-Aquila-mini.APEX-I-MiniPlus-V2.gguf`	`14.63 GB` (`13.63 GiB`)	`13.63 GiB`	Custom APEX-I (3.38 BPW)	Main agentic search, browser reasoning & logic core
`mmproj-XYZAILab_XYZ-Aquila-mini-Q8_0.gguf`	`610 MB` (`582 MiB`)	`582 MiB`	High-Precision `Q8_0` Projector	Required for browser viewport inspection, UI clicks & OCR

Base Architecture: Qwen3_5MoeForConditionalGeneration (40 layers, 256 fine-grained micro-experts with intermediate dimension 512, 8 active per token) + Vision Projector.
Active Parameters: approx. 3.2B active parameters per token (high-throughput streaming paired with 35B multimodal depth).
Importance Matrix: Calibrated on dense multimodal search traces, browser DOM interactions, and visual question answering datasets.
Memory Footprint: Lean 13.63 GiB weight footprint engineered to avoid OOM crashes on 16GB and 24GB VRAM setups.

🔬 Comparative Quantization Analysis (vs. Flat Quants & Generic APEX)

Also, don't confuse APEX-I-MiniPlus-V2 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. V2 was specifically re-engineered to avoid that quality floor (keeping core experts at calibrated IQ3_XXS, output in Q6_K, shared expert in non-linear IQ4_NL, 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.

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-V2 (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`, `IQ3_S` for `attn_qkv`	Contextual Retrieval Precision: Generic APEX reduces attention and language projections to `Q3_K` across 85% of layers. Our V2 build protects attention output in high-precision `Q6_K` and uses calibrated non-linear `IQ3_S`, ensuring flawless needle-in-a-haystack retrieval across deep 128k–256k context windows.
Attention Gates (`attn_gate.weight`)	Blindly compressed to 3-bit	Compressed to `Q3_K` (middle) / `Q4_K` (edges)	`Q8_0` (8.50 BPW)	Attention Head Stability: Attention gates modulate query-key routing across hybrid attention layers. Keeping them in 8-bit prevents attention crosstalk and hallucination over long contexts.
*Shared Foundation Expert (`ffn__shexp`)**	Flat `IQ3_S` / `Q3_K_S` (3.44 BPW)	Linear `Q4_K` (middle) / `Q5_K` (edges)	`IQ4_NL` (4.50 BPW non-linear codebook)	Foundational Knowledge Armor: The shared expert executes for 100% of tokens. In 256 micro-expert models, `IQ4_NL` non-linear codebooks preserve heavy-tailed outlier representations far better than standard linear quantization.
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)	`IQ3_S` (expanded to 10 input & 10 output layers)	Protected Ingestion & Synthesis: Half of the model's layers (10 at input, 10 at output) form a non-linear armored envelope, preventing prompt misunderstanding and token degeneration across 256 micro-experts.
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 Multimodal Vision Projector

Standard community quants frequently omit the multimodal projector or supply uncompressed FP16 files (approx. 857 MB), doubling visual memory overhead.

Bundled Q8_0 Projector: Pre-quantized to Q8_0 (582 MiB / 610 MB), saving approx. 300 MB of VRAM.
Audited Layer Fallbacks: llama.cpp automatically preserved 27 critical normalization and bias tensors in F32/F16, ensuring razor-sharp rendering of browser DOM text, minute UI action targets, and dense infographic diagrams.

💻 Everyday Laptop Benchmarks (DDR4 / DDR5 RAM)

Estimated Projections on Consumer Hardware

You do not need an enterprise server to run an autonomous multimodal web agent. Estimated throughput projections on an everyday consumer laptop (Intel Core i5 / AMD Ryzen, 4GB/6GB Laptop GPU, 32GB DDR4/DDR5 RAM):

GPU VRAM Allocation: Uses only approx. 3.8 GB VRAM (fits effortlessly on budget laptop GPUs like RTX 3050, 4050, or 2060).
System Memory Offload: Standard 32GB system RAM accommodates the remaining layers.
Estimated Document / Screenshot Ingestion (Prefill): 300 to 420+ tokens/second sustained across full viewport inputs.
Estimated Streaming Generation: 20 to 24+ tokens/second sustained output across system RAM!

🔥 The 24GB Miracle: Full 256K Context Runs In VRAM!

Autonomous web search and UI navigation rapidly fill context buffers with full-page DOM trees, image embeddings, and multi-turn action traces. Generic community quants weigh 16–19 GiB in weights alone, immediately crashing 24GB cards.

XYZ-Aquila-mini APEX-I-MiniPlus-V2 fits the entire 256K context window within 24GB VRAM:

Context Length	Model Weights (Est.)	KV Cache (q8_0, 4 slots)	Compute Buffers	Total GPU VRAM (Est.)	Hardware Feasibility
32,768 (32k)	`13.63 GiB`	`0.58 GiB`	`1.80 GiB`	`16.01 GiB`	Full offload on 24GB; partial on 16GB
65,536 (64k)	`13.63 GiB`	`0.92 GiB`	`1.95 GiB`	`16.50 GiB`	Effortless fit on 24GB GPUs
131,072 (128k)	`13.63 GiB`	`1.58 GiB`	`2.22 GiB`	`17.43 GiB`	Effortless fit on 24GB GPUs
262,144 (256k)	`13.63 GiB`	`2.92 GiB`	`2.80 GiB`	`19.35 GiB`	🔥 FULL 256K AGENT TRACE IN VRAM!

Note: Projections leave approx. 4.65 GiB of headroom on 24GB cards for display buffers and the Q8 vision projector.

🏎️ Hardware Throughput Projections (RTX 30 / 40 / 50)

Projections represent theoretical estimates derived from hardware memory bandwidth and the approx. 3.2B active parameter MoE design.

⚖️ The Speed vs. Precision Trade-off

Uncompressed F32 Router Selectors: In a 256 micro-expert model, linear bit reduction on router logits leads to expert misrouting during multimodal grounding. Gating routers (ffn_gate_inp.weight) are maintained in uncompressed F32 (2 MB/layer) to ensure visual and textual tokens are routed with zero drift.
Q6_K Output Head: Protects structured JSON schema, web search syntax, and code invocation tokens from quantization artifacts.
Non-Linear IQ Codebooks: IQ3_XXS, IQ3_S, and IQ4_NL ensure high semantic density across UI reasoning and continuous search workflows.

🛠️ Surgical Tensor Quantization Map

Tensor Pattern	Layer Scope	Quant Type	BPW	Engineering Rationale
`output.weight`	Vocabulary Head	`Q6_K`	6.56	Uncompromised 6-bit precision for web queries, structured JSON & tool syntax
`token_embd.weight`	Embedding	`High-Prec`	High	Preserves subtle token semantics and prompt grounding
`ffn_gate_inp.weight`	Expert Routers	`F32`	32.0	Uncompressed full-precision routers preventing visual token misrouting
`attn_gate.weight`	Attention Gates	`Q8_0`	8.50	High-precision 8-bit gating for attention routing dynamics
*`ffn__shexp`**	Shared Experts	`IQ4_NL`	4.50	4-bit non-linear codebook for the 100% active shared foundational expert
`ffn_down/up/gate`	Edges (0–9, 30–39)	`IQ3_S`	3.44	Armored boundary layers protecting prompt ingest and final UI action synthesis
`ffn_down/up/gate`	Core (10–29)	`IQ3_XXS`	3.06	Deep compression (272 MiB → 98 MiB per block) calibrated via multimodal imatrix
`mmproj` (Vision)	Visual Projector	`Q8_0`	8.00	High-fidelity OCR and UI coordinate rendering with 27 critical F32/F16 fallbacks
Norms & Biases	All Layers	`F32`	32.0	Absolute numerical stability across deep 40-layer computation

📖 Recommended Configuration & Setup

Unsloth Studio:

Load XYZ-Aquila-mini.APEX-I-MiniPlus-V2.gguf.
Select mmproj-XYZAILab_XYZ-Aquila-mini-Q8_0.gguf as the vision projector.
Configure KV Cache Dtype to q8_0 and Context Checkpoints to 1.
Set GPU Offload to 100% (-ngl 99) on 24GB GPUs.

llama.cpp CLI:

llama-cli -m XYZ-Aquila-mini.APEX-I-MiniPlus-V2.gguf \
  --mmproj mmproj-XYZAILab_XYZ-Aquila-mini-Q8_0.gguf \
  -ngl 99 \
  -c 32768

LM Studio / Ollama:

Load the main model and attach the bundled mmproj vision adapter.
Maximize GPU offload and define context buffer.

Downloads last month: 50

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for IsValorum/XYZ-Aquila-mini-APEX-I-MiniPlus-V2-GGUF

Base model

Qwen/Qwen3.6-35B-A3B

Finetuned

XYZAILab/XYZ-Aquila-mini

Quantized

(8)

this model

Collection including IsValorum/XYZ-Aquila-mini-APEX-I-MiniPlus-V2-GGUF

APEX-I Custom Quants

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