Text Generation
GGUF
apex
custom-quantization
unsloth-studio
Mixture of Experts
coding
agentic
code
llama.cpp
qwen35moe
quantized
quantization
imatrix
conversational
Instructions to use IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-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 IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-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-GGUF # Run inference directly in the terminal: llama cli -hf IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-GGUF
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-GGUF # Run inference directly in the terminal: llama cli -hf IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-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 IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-GGUF # Run inference directly in the terminal: ./llama-cli -hf IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-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 IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-GGUF # Run inference directly in the terminal: ./build/bin/llama-cli -hf IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-GGUF
Use Docker
docker model run hf.co/IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-GGUF
- LM Studio
- Jan
- vLLM
How to use IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-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-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-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-GGUF
- Ollama
How to use IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-GGUF with Ollama:
ollama run hf.co/IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-GGUF
- Unsloth Desktop
- Pi
How to use IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-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-GGUF
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-GGUF" } ] } } }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-GGUF with Docker Model Runner:
docker model run hf.co/IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-GGUF
- Lemonade
How to use IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-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-GGUF
Run and chat with the model
lemonade run user.KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-GGUF-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-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-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 IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-GGUF
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-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-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 "IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2-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"
Commit History
8 days ago
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Update README: Standardize independent compute and student support notice
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11 days ago
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Add official Ko-fi community compute fund and priority model requests
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23 days ago
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Show direct Ko-fi URL
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Number navigation and refine optional support
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Complete navigation index and remove duplicate support block
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Move navigation to top and clarify optional support
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Add optional Ko-fi support link
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Add optional Ko-fi support link
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26 days ago
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Add Qwen3.6 Abliterated V2.1 to lineup links
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27 days ago
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Identify MiniPlus V2.1 edition in lineup links
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Clarify established 35B MoE lineup and cross-links
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Expand V2.1 model cross-promotion
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Promote established V2.1 APEX-I-MiniPlus releases
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Add system RAM inference guidance
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28 days ago
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Restore independent benchmark references; neutralize quantizer attribution
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Remove third-party user references from model card
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Clarify external benchmark attribution
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Separate author sampling recommendation from zephel01 llmbench setting & explicit Occamy V2 references
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Replace model-specific Occamy failure example with generic checkpoint fidelity notice
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Update model-specific Delta PPL and baseline perplexity values
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Add third-party benchmark (zephel01), sampling parameters, and VRAM scaling
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Add third-party benchmark (zephel01), sampling parameters, and VRAM scaling
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Add third-party benchmark (zephel01), sampling parameters, and VRAM scaling
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Add third-party benchmark (zephel01), sampling parameters, and VRAM scaling
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Add third-party benchmark (zephel01), sampling parameters, and VRAM scaling
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Eliminate unintentional strikethrough by replacing bare tildes with explicit approx.
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Fix markdown table formatting, resolve stray backticks, and correct PPL delta attribution
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Fix perplexity wording and eliminate unrendered LaTeX math formatting
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Update Architecture Selection Guide with empirical PPL (5.5045 ± 0.1330) and direct link to V2.1
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docs: clean excessive emojis across headings and callouts for professional styling
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docs: emphasize V2.1 system RAM execution with deep context, DDR4/DDR5 scaling, and VRAM offload
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docs: add warning clarifying APEX-I-MiniPlus (all versions) vs generic community APEX-I-Mini
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docs: add Alder Lake P/E-Core testbed and desktop CPU scaling details
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docs: clean secondary note callout to match clean single-banner standard
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Clean duplicate banners, remove conflicting legacy text, and restore accurate Q8_0 specs
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Update tailored Architecture Selection Guide
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Update Architecture Selection Guide: transparent comparison of V1, V2, and V2.1
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Update README with positive MiniPlus Tier Guide and V2.1 links
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Update direct link to new IsValorum/KAT-Coder-V2.5-Dev-APEX-I-MiniPlus-V2.1-GGUF
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Set quantized_by, quantization tags, and Qwen multilingual support
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Update legacy notice to English
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Update legacy notice: excellence vs flat 3-bit and link to V2.1
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Add V2.1 roadmap announcement and anti-confusion transparency notice
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Upload README.md with huggingface_hub
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Update README.md
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