Frank
9bfa0be85d
- 31 new managed skills (anubis/arr/authentik/hermes/freetoken/podman/llm-swap runbooks etc.) now tracked. - Removed superseded skills: omp_harness_config, peon_ping_integration (replaced by user-authored peon-ping-* managed skills), rocmfp4-strix-benchmark-orchestration. - New memory-root skills: niri_compositor_ops, omp_git_workflow, omp_model_config, quadlet-media-migration, homelab-service-triage, hermes-dankclaw-ops, frank-default-large-ops, caddy-auth-dns-playbook.
1.3 KiB
1.3 KiB
name, description
| name | description |
|---|---|
| freetoken-dankclaw-build | Build FreeToken on Dankclaw (Dankware) — installs CUDA toolkit, sets CUDA_HOME, handles nvcc/CUDA version mismatch |
Build FreeToken on Dankclaw
Prerequisites
- CUDA toolkit:
sudo apt install nvidia-cuda-toolkit(gives nvcc at /usr/bin, libcudart at /usr/lib/x86_64-linux-gnu/) - uv: installed at /home/dank/.local/bin/uv (or install via
curl -LsSf https://astral.sh/uv/install.sh | sh) - CMake, git, Python >= 3.10 — all present
Build steps
cd /home/dank
git clone https://github.com/FlashML-org/FreeToken.git # or cd into existing
cd FreeToken
uv venv && source .venv/bin/activate
export CUDA_HOME=/usr
export FREETOKEN_ALLOW_CUDA_MISMATCH=1 # nvcc 12.4 + torch cu130 is safe
uv pip install -e ".[accel]"
Verification
ft --version
python3 -c "
from freetoken.checkpoint import ftw
import torch
t = torch.randn(3,4,dtype=torch.float16)
print(ftw._dtype_str(t.dtype), ftw._elsize(t.dtype))
"
Key notes
- FTW format is dtype-agnostic: FP16, BF16 (default), FP32, FP8 all work
- GGUF support: only Gemma4GGUFForCausalLM
- GGML types: F32/F16/BF16 (native views), Q4_0/Q6_K (dequant), Q8_0 (listed, no dequant)
- The NVIDIA driver modules may not be loaded at build time (NVML warning) — not a problem for the build itself