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"
File size: 2,498 Bytes
ddf8c5b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | #!/usr/bin/env bash
# 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"
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