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

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/mudler/KAT-Coder-V2.5-Dev-APEX-GGUF

LM Studio
Jan
Ollama
How to use mudler/KAT-Coder-V2.5-Dev-APEX-GGUF with Ollama:
```
ollama run hf.co/mudler/KAT-Coder-V2.5-Dev-APEX-GGUF
```

Unsloth Studio

How to use mudler/KAT-Coder-V2.5-Dev-APEX-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 mudler/KAT-Coder-V2.5-Dev-APEX-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 mudler/KAT-Coder-V2.5-Dev-APEX-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use mudler/KAT-Coder-V2.5-Dev-APEX-GGUF with Pi:

Start the llama.cpp server

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

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": "mudler/KAT-Coder-V2.5-Dev-APEX-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mudler/KAT-Coder-V2.5-Dev-APEX-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 mudler/KAT-Coder-V2.5-Dev-APEX-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 mudler/KAT-Coder-V2.5-Dev-APEX-GGUF

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use mudler/KAT-Coder-V2.5-Dev-APEX-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mudler/KAT-Coder-V2.5-Dev-APEX-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 "mudler/KAT-Coder-V2.5-Dev-APEX-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"

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

No MTP?

by gbuzhf - opened 8 days ago

Discussion

gbuzhf

8 days ago

🍀

Zeioth

6 days ago

The upstream model doesn't have MTP / DFlash headers. It would be pretty cool beign able to test, but it works very well witout it. x2 slower, but 100% solver alucinations on my end.

gbuzhf

6 days ago

•

edited 6 days ago

The upstream model doesn't have MTP / DFlash headers. It would be pretty cool beign able to test, but it works very well witout it. x2 slower, but 100% solver alucinations on my end.

I've done it already.

Base model's MTP header is perfectly fitting.

#gen tokens = 1727, #acc tokens = 1651
#gen tokens = 1820, #acc tokens = 1743
#gen tokens = 1919, #acc tokens = 1838
#gen tokens = 1661, #acc tokens = 1586
#gen tokens = 1623, #acc tokens = 1549
...

I've embedded to mudler's original APEX recipes (I-Balance and I-Compact),
and unsloth's UD-Q5_K_XL recipe,
both uploaded in my repo already.
I've also experimental v2D-lite versions with APEX versions there.

I'm also using with ngram-mod, no-burden & helps a little, but overall ''because why not together with MTP?'' ...

Thanks to @mudler , @bartowski & @unsloth for opening up their recipes and making the internet a better place.

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