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 - Removed superseded skills: omp_harness_config, peon_ping_integration (replaced by user-authored peon-ping-* managed skills), rocmfp4-strix-benchmark-orchestration.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