Instructions to use IsValorum/KAT-Coder-V2.5-Dev-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/KAT-Coder-V2.5-Dev-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/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-GGUF
# Run inference directly in the terminal:
llama cli -hf IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-GGUF
# Run inference directly in the terminal:
llama cli -hf IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-GGUF

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/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-GGUF
# Run inference directly in the terminal:
./llama-cli -hf IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-GGUF

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/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-GGUF

Use Docker

docker model run hf.co/IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-GGUF

LM Studio
Jan

vLLM

How to use IsValorum/KAT-Coder-V2.5-Dev-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/KAT-Coder-V2.5-Dev-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/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-GGUF

Ollama
How to use IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-GGUF with Ollama:
```
ollama run hf.co/IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-GGUF
```
Unsloth Desktop

How to use IsValorum/KAT-Coder-V2.5-Dev-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/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-GGUF

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/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-GGUF with Docker Model Runner:
```
docker model run hf.co/IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-GGUF
```

Lemonade

How to use IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-GGUF

Run and chat with the model

lemonade run user.KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use IsValorum/KAT-Coder-V2.5-Dev-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/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-GGUF

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/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-GGUF

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use IsValorum/KAT-Coder-V2.5-Dev-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/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-GGUF

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/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-GGUF" \
  --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 14 days ago

Commit

ce4f086

verified ·

1 Parent(s): 2addcc3

Restore independent benchmark references; neutralize quantizer attribution

Browse files

Files changed (1) hide show

README.md +25 -0

README.md CHANGED Viewed

@@ -59,6 +59,7 @@ quantized_by: IsValorum
 ## <a id="quick-navigation"></a>Quick Navigation Index
 - [Model Files & Technical Specifications](#model-specifications)
 - [Comparative Quantization Analysis (vs. Flat Quants & Generic APEX)](#comparative-analysis)
 - [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)
@@ -66,6 +67,29 @@ quantized_by: IsValorum
 - [Model Inherent Behavior vs. Quantization Fidelity Notice](#quantization-fidelity)
 ---
 <a id="model-specifications"></a>
 ## Model Files & Technical Specifications
@@ -144,6 +168,7 @@ When running with full GPU offload (`-ngl 99`), KAT-Coder's fine-grained MoE arc
 | Hardware Target | Offload Mode | Generation Speed (Est.) | Prompt Prefill Speed (Est.) | Engineering Highlights |
 | :--- | :--- | :---: | :---: | :--- |
 | **NVIDIA RTX 4090 (24GB GDDR6X)** | Full GPU (`-ngl 99`) | **80 – 105+ tok/s** | **1,800 – 2,600+ tok/s** | Near-instantaneous code completion & refactoring |
 | **NVIDIA RTX 3090 (24GB GDDR6)** | Full GPU (`-ngl 99`) | **65 – 80+ tok/s** | **1,400 – 2,000+ tok/s** | Full 256k repository context in dedicated VRAM |
 | **NVIDIA RTX 4080 / 5070 (16GB)** | Partial offload (approx. 30 layers) | **35 – 45+ tok/s** | **800 – 1,200+ tok/s** | High-efficiency local coding assistant |

 ## <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)
 - [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)
 - [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]
+> **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 model. It is included as independent evidence of the broader MiniPlus quantization approach.
+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 & Technical Specifications
 | Hardware Target | Offload Mode | Generation Speed (Est.) | Prompt Prefill Speed (Est.) | Engineering 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`) | **80 – 105+ tok/s** | **1,800 – 2,600+ tok/s** | Near-instantaneous code completion & refactoring |
 | **NVIDIA RTX 3090 (24GB GDDR6)** | Full GPU (`-ngl 99`) | **65 – 80+ tok/s** | **1,400 – 2,000+ tok/s** | Full 256k repository context in dedicated VRAM |
 | **NVIDIA RTX 4080 / 5070 (16GB)** | Partial offload (approx. 30 layers) | **35 – 45+ tok/s** | **800 – 1,200+ tok/s** | High-efficiency local coding assistant |