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 /HANDOFF.md

TessaCoil

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K3 Rental Validation — Session Handoff

Read FINDINGS.md first for all root causes / patches / decisions. This file = live state

exact next actions. Started fresh after context compaction.

Box access

vast.ai German host: ssh -o StrictHostKeyChecking=accept-new root@158.181.52.18 -p 43516
ed25519 key, passphrase loaded via askpass (do NOT echo it).
Hardware: 1× EPYC 7663 (56C/112T), 503GB RAM, 8× RTX 5060 Ti 16GB, 2.5TB NVMe (1.1TB free).
Local repo /Users/danielleensign/prog/chat/k3-test/ ⇄ box /root/k3-test/ via rsync. rsync clobbers +x — chmod 755 scripts on box after sync.

What's DONE and verified

K3 Q4_K_XL loads + generates via LLAMA_MMAP_NO_PREFETCH=1 (lazy mmap patch, FINDINGS #4).
K3+DSpark arch crash FIXED: kimi-k3.cpp patched to populate t_layer_inp (FINDINGS #1). Rebuilt llama-server on box. Patch backup: /root/llama.cpp/src/models/kimi-k3.cpp.bak.
DSpark draft invalid-token FIXED: added tokenizer.ggml.mask_token_id=163824 to draft GGUF (FINDINGS #1b). draft.gguf → symlink → draft_masked.gguf.
Confirmed trunk is Q8_0 (not 4-bit); only experts are MXFP4 (FINDINGS #7).
Measured DSpark-on-CPU = 2x LOSS (tg 0.224 vs 0.5 baseline). Need draft on GPU.
Built requant_trunk.c → /root/k3-test/requant_trunk (FINDINGS #7). Requants trunk Q8_0→Q4_K, byte-preserves MXFP4 experts + F32 norms. Validated logic on shard 2 (43 requant + 62 copy, 47.5→44.9 GB, no crash).

IN FLIGHT (check first)

Shard-2 requant test: ssh box 'cat /tmp/requant_test.log; ls -la /tmp/shard2_q4.gguf' (was still writing output, PID was 45320). Verify it completed and output parses.

NEXT ACTIONS (in order)

Verify shard-2 output correctness. Confirm /tmp/shard2_q4.gguf completed, trunk tensors are Q4_K, experts still MXFP4. Quick parse check with a GGUF reader.
Run requant_trunk across all 32 shards. Output to a new dir /root/models/Kimi-K3-GGUF/Q4TRUNK-Q4_K_XL/ with the SAME filenames (so split loading works). Process per-shard: for i in 01..32: ./requant_trunk in_shard_i out_shard_i. Shard 1 is metadata-only (n_tensors=0) — just byte-copy it as-is (tool handles 0 tensors, but verify). NOTE: shard filenames use -00001-of-00032.gguf etc. ~47GB/shard, I/O bound, consider running 2-4 shards in parallel (NVMe + 112 threads; requant is single-threaded per tensor but ggml quantize_row_q4_K_ref may use OpenMP). ~28GB total shrink.
Reload + test the Q4-trunk model: kitchen sink with the DSpark draft now able to go ON GPU (drop -ngld 0, or set -ngld 999). Expect trunk 34GB → ~94GB free VRAM across 8 cards. Re-test DSpark spec speedup vs baseline. This is the key validation.
If DSpark-on-GPU wins → that's the home architecture. Then expert pinning (--moe-pin, ~150 hottest) + residency measurement (25_expert_residency.sh).

Key box paths

llama.cpp: /root/llama.cpp (b10630 + lazy-mmap patch + kimi-k3 t_layer_inp patch). Build: cd /root/llama.cpp/build && cmake --build . --config Release -j 56 --target llama-server
llama-server: /root/llama.cpp/build/bin/llama-server; llama-quantize also built.
requant tool: /root/k3-test/requant_trunk (src requant_trunk.c)
Model: /root/models/Kimi-K3-GGUF/UD-Q4_K_XL/ (32 shards, 1.4TB)
Draft: /root/models/k3-draft/draft.gguf → draft_masked.gguf (2.4GB Q8_0, mask fixed)
Scripts: 28_kitchen_sink.sh (main experiment), logs /root/ks.log, /root/k3-test/logs/{metrics.tsv,server_ks.log,progress.log}

Current best kitchen-sink invocation (in 28_kitchen_sink.sh)

LLAMA_MMAP_NO_PREFETCH=1 llama-server --host 127.0.0.1 --port 8899 \
  -m <shard1> -ngl 999 --tensor-split 0.2,1,1,1,1,1,1,1 --cpu-moe -fa on \
  -t 112 -b 256 -ub 256 -np 1 -md <draft> -ngld 0 --spec-type draft-dspark

(With Q4 trunk: change split back toward 0.3, drop -ngld 0 to put draft on GPU.)

Constraints / prefs (from user)

Max tok/s, batch-1, 64in/64out probes (box is expensive). Q4-only (no smaller quant).
Spec + expert-offload combined is the target. ~150 experts pinned + LRU rest.
Pipe output to files; background jobs that NOTIFY (no busy polling); kill stale jobs.
Budget target $15-25, hard cap $60/day.