Instructions to use Myric/KAT-Coder-V2.5-Dev-MTP-APEX-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Myric/KAT-Coder-V2.5-Dev-MTP-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 Myric/KAT-Coder-V2.5-Dev-MTP-APEX-GGUF # Run inference directly in the terminal: llama cli -hf Myric/KAT-Coder-V2.5-Dev-MTP-APEX-GGUF
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Myric/KAT-Coder-V2.5-Dev-MTP-APEX-GGUF # Run inference directly in the terminal: llama cli -hf Myric/KAT-Coder-V2.5-Dev-MTP-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 Myric/KAT-Coder-V2.5-Dev-MTP-APEX-GGUF # Run inference directly in the terminal: ./llama-cli -hf Myric/KAT-Coder-V2.5-Dev-MTP-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 Myric/KAT-Coder-V2.5-Dev-MTP-APEX-GGUF # Run inference directly in the terminal: ./build/bin/llama-cli -hf Myric/KAT-Coder-V2.5-Dev-MTP-APEX-GGUF
Use Docker
docker model run hf.co/Myric/KAT-Coder-V2.5-Dev-MTP-APEX-GGUF
- LM Studio
- Jan
- vLLM
How to use Myric/KAT-Coder-V2.5-Dev-MTP-APEX-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Myric/KAT-Coder-V2.5-Dev-MTP-APEX-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": "Myric/KAT-Coder-V2.5-Dev-MTP-APEX-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Myric/KAT-Coder-V2.5-Dev-MTP-APEX-GGUF
- Ollama
How to use Myric/KAT-Coder-V2.5-Dev-MTP-APEX-GGUF with Ollama:
ollama run hf.co/Myric/KAT-Coder-V2.5-Dev-MTP-APEX-GGUF
- Unsloth Studio
How to use Myric/KAT-Coder-V2.5-Dev-MTP-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 Myric/KAT-Coder-V2.5-Dev-MTP-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 Myric/KAT-Coder-V2.5-Dev-MTP-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 Myric/KAT-Coder-V2.5-Dev-MTP-APEX-GGUF to start chatting
- Pi
How to use Myric/KAT-Coder-V2.5-Dev-MTP-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 Myric/KAT-Coder-V2.5-Dev-MTP-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": "Myric/KAT-Coder-V2.5-Dev-MTP-APEX-GGUF" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Myric/KAT-Coder-V2.5-Dev-MTP-APEX-GGUF with Docker Model Runner:
docker model run hf.co/Myric/KAT-Coder-V2.5-Dev-MTP-APEX-GGUF
- Lemonade
How to use Myric/KAT-Coder-V2.5-Dev-MTP-APEX-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Myric/KAT-Coder-V2.5-Dev-MTP-APEX-GGUF
Run and chat with the model
lemonade run user.KAT-Coder-V2.5-Dev-MTP-APEX-GGUF-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use Myric/KAT-Coder-V2.5-Dev-MTP-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 Myric/KAT-Coder-V2.5-Dev-MTP-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 Myric/KAT-Coder-V2.5-Dev-MTP-APEX-GGUF
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Myric/KAT-Coder-V2.5-Dev-MTP-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 Myric/KAT-Coder-V2.5-Dev-MTP-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 "Myric/KAT-Coder-V2.5-Dev-MTP-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"
Why Q3_K for ssm.apha/beta?
blk.0.ssm_alpha.weight [2 048, 32] Q3_K
blk.0.ssm_beta.weight [2 048, 32] Q3_K
ssm.alpha/beta are recommended to be kept in F32 and they have small size. Why did you use quality breaking values?
Those are the recommendations, but I used an actual measured sensitivity. I sanity check each model with real agentic coding loops before uploading. Are you seeing problems with that model?
l tried a few tests with the "quality" version and found it performing worse than original 3.6 35B A3B I'm using right now. Too many bugs, syntax errors, wrong initialization order, etc. Just compared againts recently released Qwen 3.8 by unsloth and found there
ssm.alpha IQ4_XS
ssm.beta IQ1_M
GPT searched for source code and found:
beta = ssm_beta · x
beta = sigmoid(beta)
alpha = ssm_alpha · x
alpha = alpha + dt
alpha = softplus(alpha)
gate = alpha * A
The conclusion was, that modern Unsloth's decision to keep alpha in higher quants is reasonable.
How to you check the sensitivity of the layers?
Kat is one I mostly produced to demonstrate that the Qwen MTP worked with a reasonable acceptance rate. (Kat coder is published without MTP). I'll take a look at it.
So I did rebuild with F32 and my benchmarks improved (The old version got a perfect score on my programming benchmarks, but the new version took half as many tokens for the same result.) Good catch.
I'm also rerunning an MTP settings sweep but expect n=2 will be optimum like it is for other Qwen 3.6 derivatives with the recommended temperature. If you run it cooler the MTP helps more.
Update - Nope n=3 was about 1% better on my sweep.
What's your hardware you're running the model on?
These run great on my 16GB rtx 4060 with offloading and of course they fit fine on the dgx spark. I have even run them CPU only and they're slow but usable. These are typically bandwidth bound, so smaller => faster; bigger => slightly smarter.
@Myric , thanks for rebuilding and alpha/beta role confirmation. I usually try hard test prompt:
Write a single-file HTML/JS/CSS Windows-style desktop application with six apps, a start menu, a task manager, and a console. No external dependencies. The whole desktop and each application must be ui friendly ,beautiful, fully functional, meaningful and useful, including all tabls and settings. Output to desktop.html
It's hard for model to mix html, css, javascript in a single 1500 lines file.