Instructions to use TessaCoil/K3-Stuff 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 TessaCoil/K3-Stuff 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 TessaCoil/K3-Stuff:Q8_0 # Run inference directly in the terminal: llama cli -hf TessaCoil/K3-Stuff:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf TessaCoil/K3-Stuff:Q8_0 # Run inference directly in the terminal: llama cli -hf TessaCoil/K3-Stuff:Q8_0
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 TessaCoil/K3-Stuff:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf TessaCoil/K3-Stuff:Q8_0
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 TessaCoil/K3-Stuff:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf TessaCoil/K3-Stuff:Q8_0
Use Docker
docker model run hf.co/TessaCoil/K3-Stuff:Q8_0
- LM Studio
- Jan
- Ollama
How to use TessaCoil/K3-Stuff with Ollama:
ollama run hf.co/TessaCoil/K3-Stuff:Q8_0
- Unsloth Desktop
- Pi
How to use TessaCoil/K3-Stuff with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf TessaCoil/K3-Stuff:Q8_0
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": "TessaCoil/K3-Stuff:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use TessaCoil/K3-Stuff with Docker Model Runner:
docker model run hf.co/TessaCoil/K3-Stuff:Q8_0
- Lemonade
How to use TessaCoil/K3-Stuff with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull TessaCoil/K3-Stuff:Q8_0
Run and chat with the model
lemonade run user.K3-Stuff-Q8_0
List all available models
lemonade list
- Hermes Agent
How to use TessaCoil/K3-Stuff with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf TessaCoil/K3-Stuff:Q8_0
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 TessaCoil/K3-Stuff:Q8_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use TessaCoil/K3-Stuff with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf TessaCoil/K3-Stuff:Q8_0
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 "TessaCoil/K3-Stuff:Q8_0" \ --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"
| # k3-test/config.env — shared configuration for all scripts. | |
| # Override any value by exporting it before running, e.g. K3_INCLUDE="*UD-IQ1_S*" ./01_download_k3.sh | |
| # --- pinned runtime ------------------------------------------------------- | |
| # llama.cpp master @ 2026-08-25 — includes "model: add Kimi-K3 text model" (#26185, merged 2026-08-15). | |
| # Vision support needs the unsloth fork PR#48 instead; this test plan is text-only => mainline is fine. | |
| LLAMA_GIT="https://github.com/ggml-org/llama.cpp" | |
| LLAMA_REF="d222767c7a6516559a3f49e7721b6c6b1acc87b4" | |
| LLAMA_DIR="${LLAMA_DIR:-$HOME/llama.cpp}" | |
| LLAMA_BIN="${LLAMA_BIN:-$LLAMA_DIR/build/bin}" | |
| # --- models ---------------------------------------------------------------- | |
| MODEL_DIR="${MODEL_DIR:-$HOME/models}" | |
| K3_HF_REPO="unsloth/Kimi-K3-GGUF" | |
| K3_INCLUDE="${K3_INCLUDE:-*UD-Q4_K_XL*}" # 32 shards, 1508.7 GB total (verified 2026-08-25) | |
| K3_SUBDIR="UD-Q4_K_XL" | |
| K3_FIRST_SHARD="${K3_FIRST_SHARD:-Kimi-K3-UD-Q4_K_XL-00001-of-00032.gguf}" | |
| K3_MODEL_PATH="${K3_MODEL_PATH:-$MODEL_DIR/Kimi-K3-GGUF/$K3_SUBDIR/$K3_FIRST_SHARD}" | |
| K3_VISION_MMPROJ="${K3_VISION_MMPROJ:-}" # set to download mmproj-BF16.gguf if vision is wanted | |
| SMOKE_HF_REPO="Qwen/Qwen3-30B-A3B-GGUF" | |
| SMOKE_INCLUDE="${SMOKE_INCLUDE:-*Q4_K_M*}" | |
| SMOKE_MODEL_PATH="${SMOKE_MODEL_PATH:-$MODEL_DIR/Qwen3-30B-A3B/Qwen3-30B-A3B-Q4_K_M.gguf}" | |
| # --- runtime knobs ---------------------------------------------------------- | |
| THREADS="${THREADS:-$(nproc)}" | |
| GEN_TOKENS="${GEN_TOKENS:-128}" # tokens to decode per experiment run | |
| CTX_SIZE="${CTX_SIZE:-8192}" | |
| SEED="${SEED:-42}" | |
| # trunk placement (see PREP_REPORT.md "trunk VRAM reality check"): | |
| # UD-Q4_K_XL dense trunk is mostly Q8_0 (~55-60 GB) — does NOT fit in 2x24GB. | |
| # trunk across 3 GPUs : TSPLIT="1,1,1" TGPU_N=3 | |
| # trunk across 2 GPUs : TSPLIT="1,1" (spill to CPU for the remainder) | |
| TSPLIT="${TSPLIT:-1,1,1,1}" # 4x16GB=64GB holds the ~58GB Q8_0 trunk; 8x5060Ti box | |
| # expert override: keep every routed-expert tensor on CPU (equivalent to --cpu-moe) | |
| EXPERT_CPU_OT='.ffn_(up|gate|down)_exps.=CPU' | |
| # sampling (deterministic for comparability) | |
| SAMPLE_ARGS="--temp 0 --top-k 1 --seed $SEED" | |
| # --- logging ---------------------------------------------------------------- | |
| K3TEST_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" | |
| LOG_ROOT="${LOG_ROOT:-$K3TEST_ROOT/logs}" | |
| MON_INTERVAL="${MON_INTERVAL:-10}" # seconds between resource samples | |
| mkdir -p "$LOG_ROOT" | |