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: 1,940 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/prebox_check.sh — 45-second GO/NO-GO check to run FIRST on any fresh box.
# Verifies the three things that can silently burn a rental day (learned the hard way
# on instance 48707384: HF CDN throttled to ~7 KB/s, i.e. un-rentable for K3):
# 1. HF CDN download speed (K3 lives there) — need > ~8 MB/s, want > 100 MB/s
# 2. generic internet speed (Cloudflare) — sanity reference
# 3. github speed (llama.cpp clone) — need > ~2 MB/s
# Usage: ssh in, curl|-bash this file or copy it over, read the verdict.
set -u
MBS() { awk -v b="$1" 'BEGIN{printf "%.1f MB/s (%.0f Mbps)", b/1048576, b*8/1e6}'; }
CF=$(curl -so /dev/null -w "%{speed_download}" --max-time 20 https://speed.cloudflare.com/__down?bytes=26214400 2>/dev/null || echo 0)
GH=$(curl -so /dev/null -w "%{speed_download}" --max-time 25 -L https://github.com/ggml-org/llama.cpp/archive/refs/heads/master.tar.gz 2>/dev/null || echo 0)
URL=https://huggingface.co/Qwen/Qwen3-30B-A3B-GGUF/resolve/main/Qwen3-30B-A3B-Q4_K_M.gguf
HF=$(curl -so /dev/null -w "%{speed_download}" --max-time 25 -r 0-52428799 "$URL" 2>/dev/null || echo 0)
if (( ${HF%.*} < 8000000 )) && [[ -n "${HF_TOKEN:-}" ]]; then
HF_AUTH=$(curl -so /dev/null -w "%{speed_download}" --max-time 25 -r 0-52428799 \
-H "Authorization: Bearer ${HF_TOKEN}" "$URL" 2>/dev/null || echo 0)
(( ${HF_AUTH%.*} > ${HF%.*} )) && HF=$HF_AUTH
fi
echo "cloudflare: $(MBS "${CF%.*}")"
echo "github: $(MBS "${GH%.*}")"
echo "huggingface: $(MBS "${HF%.*}")$([[ -n "${HF_TOKEN:-}" ]] && echo " (with token)") <-- K3 download path"
if (( ${HF%.*} > 100000000 )); then echo "VERDICT: GO — HF is fast, 1.5 TB ≈ 3.5 h or less"
elif (( ${HF%.*} > 8000000 )); then echo "VERDICT: MARGINAL — HF usable but slow; consider hf_transfer and a long tmux session"
else echo "VERDICT: NO-GO — HF CDN is throttled/broken from this host. Destroy and re-rent (plan §7)."
fi
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