Instructions to use KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-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 KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-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 KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-GGUF:IQ2_M
# Run inference directly in the terminal:
llama cli -hf KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-GGUF:IQ2_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-GGUF:IQ2_M
# Run inference directly in the terminal:
llama cli -hf KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-GGUF:IQ2_M

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 KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-GGUF:IQ2_M
# Run inference directly in the terminal:
./llama-cli -hf KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-GGUF:IQ2_M

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 KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-GGUF:IQ2_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-GGUF:IQ2_M

Use Docker

docker model run hf.co/KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-GGUF:IQ2_M

LM Studio
Jan

vLLM

How to use KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-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": "KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-GGUF:IQ2_M

Ollama
How to use KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-GGUF with Ollama:
```
ollama run hf.co/KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-GGUF:IQ2_M
```

Unsloth Studio

How to use KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-GGUF to start chatting

How to use KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-GGUF:IQ2_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-GGUF:IQ2_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-GGUF:IQ2_M

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 "KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-GGUF:IQ2_M" \
  --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"

Docker Model Runner
How to use KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-GGUF with Docker Model Runner:
```
docker model run hf.co/KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-GGUF:IQ2_M
```

Lemonade

How to use KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-GGUF:IQ2_M

Run and chat with the model

lemonade run user.KAT-Coder-V2.5-Dev-IQ2_M-GGUF-IQ2_M

List all available models

lemonade list

Hermes Agent

How to use KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-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 KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-GGUF:IQ2_M

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 KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-GGUF:IQ2_M

Run Hermes

hermes

Atomic Chat

KAT-Coder-V2.5-Dev — IQ2_M GGUF

A single imatrix-guided IQ2_M quantization of Kwaipilot/KAT-Coder-V2.5-Dev, built to put a 34.7B mixture-of-experts coder inside roughly 11 GB.


file	`KAT-Coder-V2.5-Dev-IQ2_M.gguf`
size	10.85 GiB
architecture	`qwen35moe` (MoE, ~3B active)
parameters	34.66 B
native context	262,144
imatrix	yes — `katcoder.imatrix` included

What is and isn't verified

Verified: the file loads under llama.cpp and generates — prompt processing and token generation both exercised, exit code 0.

Not measured: there is no KL-divergence or perplexity table for this quant. IQ2_M is an aggressive tier and quality loss at ~2.7 bits per weight is real; this repo does not quantify it. If you want a quant release with a measured fidelity table, see Qwen3.6-27B-GGUF, where every tier is compared against the BF16 reference.

Treat this as "a working IQ2_M with its imatrix", not as a validated one.

Run it

llama-cli -m KAT-Coder-V2.5-Dev-IQ2_M.gguf -c 32768 -no-cnv -p "write a binary search in Rust"

-no-cnv matters: recent llama-cli builds otherwise drop into conversation mode and wait on stdin.

Note on metadata

The GGUF carries general.name = "Src", an artifact of the directory it was converted from. It is cosmetic and does not affect inference.

Credit

Model and weights © Kwaipilot — KAT-Coder-V2.5-Dev, Apache-2.0. This repo adds only the quantization. Weights otherwise unmodified.

Downloads last month: -

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

2-bit

Model tree for KikoCis/KAT-Coder-V2.5-Dev-IQ2_M-GGUF

Base model

Kwaipilot/KAT-Coder-V2.5-Dev

Quantized

(61)

this model