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: 1,192 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 | #!/usr/bin/env bash
# k3-test/27_mini.sh — minimal FA/B-thread knob test, cheap prompts, max_itt
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
export K3TEST_ROOT="$SCRIPT_DIR"
source "$SCRIPT_DIR/lib/common.sh"
source "$K3TEST_ROOT/lib/server.sh"
set -uo pipefail
[[ -f $K3_MODEL_PATH ]] || die "K3 missing"
MINI_PROMPT="$K3TEST_ROOT/prompts/gen/coding_64tok.txt"
[[ -f $MINI_PROMPT ]] || MINI_PROMPT="$K3TEST_ROOT/prompts/coding.txt"
BASE_SPLIT="0.3,1,1,1,1,1,1,1"
FA_FLAGS=()
[[ "${SRV_FA:-0}" == "1" ]] && FA_FLAGS=(-fa on)
BASE=( -m "$K3_MODEL_PATH" -ngl 999 --tensor-split "$BASE_SPLIT" "${FA_FLAGS[@]}" --cpu-moe -b 256 -ub 256 )
THREADS="${THREADS:-112}"
PRED="${PRED:-64}"
SEL="${SRV_GROUPS:-M}"
# mini group M: fa on/off + cold/warm pair (~in 1-3 min per probe)
if [[ " $SEL " == *" M "* ]]; then
info "### mini: fa=$( [[ ${SRV_FA:-0} == 1 ]] && echo ON || echo OFF )"
if start_server "M" "${BASE[@]}" -np 1 -t "$THREADS"; then
server_completion "mini_cold_fa${SRV_FA:-0}" "$MINI_PROMPT" "$PRED"
server_completion "mini_warm_fa${SRV_FA:-0}" "$MINI_PROMPT" "$PRED"
stop_server
fi
echo "$(date +%s) mini_done" >> "$LOG_ROOT/k3_suite_status.txt"
fi
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