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

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

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-Stuff / scripts /prebox_check.sh

TessaCoil

Upload folder using huggingface_hub

ddf8c5b verified 3 days ago

Raw

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1.94 kB

	#!/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