---
name: freetoken-dankclaw-build
description: "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
```bash
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
```bash
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