Instructions to use IsValorum/Occamy-1.0-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/Occamy-1.0-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/Occamy-1.0-APEX-I-MiniPlus-V2-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf IsValorum/Occamy-1.0-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/Occamy-1.0-APEX-I-MiniPlus-V2-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf IsValorum/Occamy-1.0-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/Occamy-1.0-APEX-I-MiniPlus-V2-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf IsValorum/Occamy-1.0-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/Occamy-1.0-APEX-I-MiniPlus-V2-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf IsValorum/Occamy-1.0-APEX-I-MiniPlus-V2-GGUF:Q8_0

Use Docker

docker model run hf.co/IsValorum/Occamy-1.0-APEX-I-MiniPlus-V2-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use IsValorum/Occamy-1.0-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/Occamy-1.0-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/Occamy-1.0-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/Occamy-1.0-APEX-I-MiniPlus-V2-GGUF:Q8_0

Ollama
How to use IsValorum/Occamy-1.0-APEX-I-MiniPlus-V2-GGUF with Ollama:
```
ollama run hf.co/IsValorum/Occamy-1.0-APEX-I-MiniPlus-V2-GGUF:Q8_0
```
Unsloth Desktop

How to use IsValorum/Occamy-1.0-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/Occamy-1.0-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/Occamy-1.0-APEX-I-MiniPlus-V2-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use IsValorum/Occamy-1.0-APEX-I-MiniPlus-V2-GGUF with Docker Model Runner:
```
docker model run hf.co/IsValorum/Occamy-1.0-APEX-I-MiniPlus-V2-GGUF:Q8_0
```

Lemonade

How to use IsValorum/Occamy-1.0-APEX-I-MiniPlus-V2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull IsValorum/Occamy-1.0-APEX-I-MiniPlus-V2-GGUF:Q8_0

Run and chat with the model

lemonade run user.Occamy-1.0-APEX-I-MiniPlus-V2-GGUF-Q8_0

List all available models

lemonade list

Hermes Agent

How to use IsValorum/Occamy-1.0-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/Occamy-1.0-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/Occamy-1.0-APEX-I-MiniPlus-V2-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use IsValorum/Occamy-1.0-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/Occamy-1.0-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/Occamy-1.0-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"

ARCHITECTURE SELECTION GUIDE — MINIPLUS V2 & V2.1 EDITIONS

This repository hosts the MiniPlus V2 edition of Occamy-1.0. Our releases are precision-engineered for specific hardware budgets and memory topologies. V2 is NOT obsolete or "worse"; each edition serves distinct inference requirements:

MiniPlus V2 (High Theoretical Layer Protection): On paper, V2 provides extra protective envelopes on edge layers (10 layers in IQ3_S + IQ4_NL shared experts + Q8_0 attention gates). However, in practical inference benchmarks—even across extreme long-context windows exceeding +160K tokens—there is virtually NO perceptible difference in quality or reasoning compared to V2.1.

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 context windows (up to 256k tokens). By replacing non-linear codebooks with linear Q3_K edge experts, keeping Q8_0 attention gates, and upgrading shared foundation experts to Q5_K across all 40 layers, it completely eliminates AVX2 CPU dequantization stalls (+24 to 28+ tok/s streaming). 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 OCR and image parsing. It provides this massive RAM streaming acceleration for only ~100 MB more, which is completely negligible in system RAM.

Which one should you choose?

If you offload 100% into GPU VRAM (24GB+ VRAM, -ngl 99): Both V2 and V2.1 run blistering fast on GPU tensor cores with virtually identical top-tier intelligence. V2 is an exceptional build for full VRAM offload.

If you run with most/all layers in system RAM (DDR4/DDR5): V2.1 is strongly recommended to eliminate CPU AVX2 lookup latency and achieve peak streaming speeds.

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 Occamy-1.0 optimized for system RAM streaming, visit: IsValorum/Occamy-1.0-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 & Specifications
Comparative Quantization Analysis (vs. Flat Quants & Generic APEX)
Bundled Q8_0 High-Precision 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

Model Files & Specifications

File Name	File Size	Memory Footprint	BPW	Description
`Occamy-1.0.APEX-I-MiniPlus-V2.gguf`	`14.62 GB` (`13.62 GiB`)	`13.62 GiB`	3.38 BPW	Core hybrid linear attention, math, logic & multimodal vision
`mmproj-Accio-Lab_occamy-1.0-Q8_0.gguf`	`614 MB` (`585 MiB`)	`585 MiB`	8.50 BPW	Dedicated `Q8_0` vision projector for optical document parsing

Base Architecture: Qwen3_5MoeForConditionalGeneration (hybrid linear attention with DeltaNet recurrent layers and 256 micro-experts).
Active Parameters: approx. 3.2B active parameters per token.
Memory Footprint: 13.62 GiB weight size engineered to fit 256K context natively in 24GB VRAM.

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 Vision Projector

Standard community uploads often omit the multimodal projector or supply uncompressed FP16 files (approx. 857 MB), bloating memory.

Bundled Q8_0 Projector: Pre-quantized to Q8_0 (585 MiB / 614 MB), saving approx. 300 MB of VRAM.
Audited Layer Fallbacks: llama.cpp automatically preserved 27 critical normalization and embedding tensors in F32/F16, ensuring razor-sharp OCR of tiny contract footnotes, financial balance sheets, and scanned legal filings without artifacts.

Everyday Laptop Benchmarks (DDR4 / DDR5 RAM)

Estimated Projections on Consumer Hardware

GPU VRAM Allocation: Uses only approx. 3.8 GB VRAM (fits effortlessly on budget laptop GPUs).
System Memory Offload: Standard 32GB system RAM accommodates the remaining layers.
Estimated Document Ingestion (Prefill): 300 to 410+ tokens/second sustained across dense inputs.
Estimated Streaming Generation: 20 to 22.5+ tokens/second sustained output across system RAM!

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

Occamy 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)	`13.62 GiB`	`0.58 GiB`	`1.80 GiB`	`16.00 GiB`	Full offload on 24GB; partial on 16GB
65,536 (64k)	`13.62 GiB`	`0.92 GiB`	`1.95 GiB`	`16.49 GiB`	Effortless fit on 24GB GPUs
131,072 (128k)	`13.62 GiB`	`1.58 GiB`	`2.22 GiB`	`17.42 GiB`	Effortless fit on 24GB GPUs
262,144 (256k)	`13.62 GiB`	`2.92 GiB`	`2.80 GiB`	`19.34 GiB`	FULL 256K NATIVE IN VRAM!

Note: Leaves approx. 4.66 GiB of headroom for display drivers and the Q8 vision projector on 24GB cards.

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`)	110 – 135+ tok/s	2,500 – 3,600+ tok/s	Blistering throughput on GDDR7 bandwidth
NVIDIA RTX 4090 (24GB GDDR6X)	Full GPU (`-ngl 99`)	80 – 105+ tok/s	1,800 – 2,600+ tok/s	Linear attention layers slash prefill latency
NVIDIA RTX 3090 (24GB GDDR6)	Full GPU (`-ngl 99`)	66 – 80+ tok/s	1,400 – 2,000+ tok/s	Full 256k native window in VRAM
Consumer Laptop (RTX 3050 + DDR4)	Hybrid (3.8GB VRAM)	20 – 22.5+ tok/s	300 – 410+ tok/s	Smooth streaming from system RAM