Instructions to use thread13/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF-MTP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thread13/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF-MTP with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="thread13/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF-MTP",
	filename="Kwaipilot_KAT-Coder-V2.5-Dev-IQ4_XS.bartowski.mtp.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use thread13/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF-MTP 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 thread13/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF-MTP:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf thread13/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF-MTP:IQ4_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf thread13/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF-MTP:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf thread13/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF-MTP:IQ4_XS

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 thread13/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF-MTP:IQ4_XS
# Run inference directly in the terminal:
./llama-cli -hf thread13/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF-MTP:IQ4_XS

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 thread13/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF-MTP:IQ4_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf thread13/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF-MTP:IQ4_XS

Use Docker

docker model run hf.co/thread13/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF-MTP:IQ4_XS

LM Studio
Jan
Ollama
How to use thread13/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF-MTP with Ollama:
```
ollama run hf.co/thread13/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF-MTP:IQ4_XS
```

Unsloth Studio

How to use thread13/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF-MTP 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 thread13/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF-MTP 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 thread13/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF-MTP to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for thread13/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF-MTP to start chatting

How to use thread13/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF-MTP with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf thread13/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF-MTP:IQ4_XS

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": "thread13/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF-MTP:IQ4_XS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use thread13/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF-MTP with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf thread13/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF-MTP:IQ4_XS

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 thread13/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF-MTP:IQ4_XS

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use thread13/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF-MTP with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf thread13/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF-MTP:IQ4_XS

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 "thread13/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF-MTP:IQ4_XS" \
  --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 thread13/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF-MTP with Docker Model Runner:
```
docker model run hf.co/thread13/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF-MTP:IQ4_XS
```

Lemonade

How to use thread13/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF-MTP with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull thread13/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF-MTP:IQ4_XS

Run and chat with the model

lemonade run user.Kwaipilot_KAT-Coder-V2.5-Dev-GGUF-MTP-IQ4_XS

List all available models

lemonade list

About

What you probably want instead is one of the APEX gguf builds by gbuzhf:

gbuzhf/KAT-Coder-V2.5-Dev-APEX-MTP-GGUF

However, sometimes you want to see if a more classic quantization will be more suitable for your specific task, or simply want to fit (or use) a specific vram target. In this case, "one can't go wrong with bartowski builds (c) reddit" -- except for these are sadly missing any draft models, and a 20-30 %% gain in inference speed is not something to easily give up on.

Hence, I'll be adding here some "franken-merges" of original bartowski builds with 20 MTP layers added to it from unsloth/Qwen3.6-35B-A3B-MTP-GGUF model using a [conversion script] made by buzz.

All uploaded models were tested and add a ~20-30 % gain in inference speed compared to original ("ungrafted") models on AMD hardware.

(It is strongly recommended to use at most q8_0 quantizations for KV cache if your hardware supoorts it, especially if it has a chance to be more performant on it.)

// TODO: add invocation examples //

Downloads last month: 558

GGUF

Model size

36B params

Architecture

qwen35moe

Hardware compatibility

4-bit

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