Instructions to use IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2.1-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-V2.1-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-V2.1-GGUF:Q4_0
# Run inference directly in the terminal:
llama cli -hf IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2.1-GGUF:Q4_0

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-V2.1-GGUF:Q4_0
# Run inference directly in the terminal:
llama cli -hf IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2.1-GGUF:Q4_0

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2.1-GGUF:Q4_0
# Run inference directly in the terminal:
./build/bin/llama cli -hf IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2.1-GGUF:Q4_0

Use Docker

docker model run hf.co/IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2.1-GGUF:Q4_0

Llama new
LM Studio
Jan

vLLM

How to use IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2.1-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-V2.1-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-V2.1-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-V2.1-GGUF:Q4_0

Ollama
How to use IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2.1-GGUF with Ollama:
```
ollama run hf.co/IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2.1-GGUF:Q4_0
```
Unsloth Desktop

How to use IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2.1-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-V2.1-GGUF:Q4_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/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2.1-GGUF:Q4_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2.1-GGUF with Docker Model Runner:
```
docker model run hf.co/IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2.1-GGUF:Q4_0
```

Lemonade

How to use IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2.1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2.1-GGUF:Q4_0

Run and chat with the model

lemonade run user.KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2.1-GGUF-Q4_0

List all available models

lemonade list

Hermes Agent

How to use IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2.1-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-V2.1-GGUF:Q4_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/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2.1-GGUF:Q4_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2.1-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-V2.1-GGUF:Q4_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/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2.1-GGUF:Q4_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 Sep 21

Commit

8c532a4

verified ·

1 Parent(s): aa957f8

Remove third-party user references from model card

Browse files

Files changed (1) hide show

README.md +0 -30

README.md CHANGED Viewed

@@ -39,7 +39,6 @@ pipeline_tag: text-generation
 > ### THE DEFINITIVE SPECIFICATION IN THE 13–14 GB CEILING
 > This **APEX-I-MiniPlus-V2.1** release represents the absolute technological limit of sparse Mixture-of-Experts quantization within the **13–14 GB envelope**. Every single tensor of its 40 layers and 256 micro-experts has been mathematically audited to maximize reasoning precision, eliminate recurrence state drift, and prevent AVX2 CPU dequantization stalls.
 > [!TIP]
 > ### 🏆 THE QUANTIZATION SWEET SPOT: Q5–Q6 FIDELITY AT 3-BIT FOOTPRINT
 > **Why APEX-I-MiniPlus V2.1 outperforms standard community quants:**
@@ -83,7 +82,6 @@ We maintain our previous releases publicly as a transparent engineering record o
 - [Model Files & Technical Specifications](#model-specifications)
 - [Empirical Benchmarks & Fidelity Verification](#empirical-benchmarks)
 - [Quality Spectrum: APEX-I-MiniPlus vs. Standard Flat Quants](#quality-spectrum)
-- [Independent Benchmark of the APEX-I-MiniPlus Family (Occamy V2 Reference)](#independent-benchmark)
 - [The 24GB Miracle: Full 256K Context Runs In VRAM!](#context-scaling)
 - [Hardware Throughput & Offload Benchmarks (RTX 30 / 40 / 50)](#throughput-projections)
 - [Surgical Tensor Quantization Map (Audited from GGUF)](#tensor-map)
@@ -92,29 +90,6 @@ We maintain our previous releases publicly as a transparent engineering record o
 - [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 V2.1 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>
 <a id="empirical-benchmarks"></a>
 ## Empirical Benchmarks & Fidelity Verification
@@ -141,7 +116,6 @@ How the handcrafted **APEX-I-MiniPlus V2.1** architecture compares against stand
 | **Standard Q3_K_M / Q3_K_S** | 3.44 bpw | **16.2 GB** | approx. +0.40 to +0.85 | Noticeable syntax drop, bracket corruption, and tokenizer classification noise. |
 | **Standard IQ2_S / Generic APEX Mini** | 2.50 bpw | approx. 12.5 GB | approx. +1.50 to +3.00+ | Severe reasoning breakdown, high perplexity spikes in `<think>` chains. |
 ## Model Files & Technical Specifications
 | File Name | File Size | Memory Footprint | BPW | Description |
@@ -184,7 +158,6 @@ How the handcrafted **APEX-I-MiniPlus V2.1** architecture compares against stand
 | 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`) | **90 – 115+ tok/s** | **2,000 – 2,800+ tok/s** | Linear attention layers slash prefill latency |
 | **NVIDIA RTX 3090 (24GB GDDR6)** | Full GPU (`-ngl 99`) | **72 – 88+ tok/s** | **1,500 – 2,200+ tok/s** | Full 256k native window in VRAM |
 | **Workstation / Laptop (DDR4 / DDR5 RAM)** | Hybrid Offload (Few layers in VRAM) | Hardware-dependent | Hardware-dependent | Zero AVX2 CPU stalls; efficient streaming from system RAM |
@@ -212,9 +185,6 @@ How the handcrafted **APEX-I-MiniPlus V2.1** architecture compares against stand
 | **262,144 (256k)**| `13.74 GiB` | `2.92 GiB` | `2.80 GiB` | **`19.46 GiB`** | **FULL 256K NATIVE IN VRAM!** |
 *Note: Leaves comfortable headroom for display drivers and compute buffers on standard 24GB GPUs (RTX 3090, RTX 4090, RTX 5090).*
-> [!TIP]
-> ### 💡 Empirical 16GB GPU Verification (Single Stream / Desktop)
-> While theoretical multi-slot server buffers estimate approx. 16.5 GiB, independent hardware testing by zephel01 on an RTX 5090 (Occamy V2 Reference) confirmed that single-stream desktop inference consumes **only 14.6 GB at 32,768 ctx** and **only 15.1 GB at 65,536 ctx** (`-ctk q8_0 -ctv q8_0 -fa on`). This empirically proves that **full 65K context runs completely in VRAM on 16GB cards (RTX 4080 / RTX 5080)** without system RAM offload!
 ---

 > ### THE DEFINITIVE SPECIFICATION IN THE 13–14 GB CEILING
 > This **APEX-I-MiniPlus-V2.1** release represents the absolute technological limit of sparse Mixture-of-Experts quantization within the **13–14 GB envelope**. Every single tensor of its 40 layers and 256 micro-experts has been mathematically audited to maximize reasoning precision, eliminate recurrence state drift, and prevent AVX2 CPU dequantization stalls.
 > [!TIP]
 > ### 🏆 THE QUANTIZATION SWEET SPOT: Q5–Q6 FIDELITY AT 3-BIT FOOTPRINT
 > **Why APEX-I-MiniPlus V2.1 outperforms standard community quants:**
 - [Model Files & Technical Specifications](#model-specifications)
 - [Empirical Benchmarks & Fidelity Verification](#empirical-benchmarks)
 - [Quality Spectrum: APEX-I-MiniPlus vs. Standard Flat Quants](#quality-spectrum)
 - [The 24GB Miracle: Full 256K Context Runs In VRAM!](#context-scaling)
 - [Hardware Throughput & Offload Benchmarks (RTX 30 / 40 / 50)](#throughput-projections)
 - [Surgical Tensor Quantization Map (Audited from GGUF)](#tensor-map)
 - [Model Inherent Behavior vs. Quantization Fidelity Notice](#quantization-fidelity)
 ---
 <a id="model-specifications"></a>
 <a id="empirical-benchmarks"></a>
 ## Empirical Benchmarks & Fidelity Verification
 | **Standard Q3_K_M / Q3_K_S** | 3.44 bpw | **16.2 GB** | approx. +0.40 to +0.85 | Noticeable syntax drop, bracket corruption, and tokenizer classification noise. |
 | **Standard IQ2_S / Generic APEX Mini** | 2.50 bpw | approx. 12.5 GB | approx. +1.50 to +3.00+ | Severe reasoning breakdown, high perplexity spikes in `<think>` chains. |
 ## Model Files & Technical Specifications
 | File Name | File Size | Memory Footprint | BPW | Description |
 | Hardware Target | Offload Mode | Generation Speed (Est.) | Prompt Prefill Speed (Est.) | Highlights |
 | :--- | :--- | :---: | :---: | :--- |
 | **NVIDIA RTX 4090 (24GB GDDR6X)** | Full GPU (`-ngl 99`) | **90 – 115+ tok/s** | **2,000 – 2,800+ tok/s** | Linear attention layers slash prefill latency |
 | **NVIDIA RTX 3090 (24GB GDDR6)** | Full GPU (`-ngl 99`) | **72 – 88+ tok/s** | **1,500 – 2,200+ tok/s** | Full 256k native window in VRAM |
 | **Workstation / Laptop (DDR4 / DDR5 RAM)** | Hybrid Offload (Few layers in VRAM) | Hardware-dependent | Hardware-dependent | Zero AVX2 CPU stalls; efficient streaming from system RAM |
 | **262,144 (256k)**| `13.74 GiB` | `2.92 GiB` | `2.80 GiB` | **`19.46 GiB`** | **FULL 256K NATIVE IN VRAM!** |
 *Note: Leaves comfortable headroom for display drivers and compute buffers on standard 24GB GPUs (RTX 3090, RTX 4090, RTX 5090).*
 ---