PlaidQ 0.7B 16-step GGUF (ggmlc)

Continuous latent diffusion code tab-completion model compiled from fredzzp/plaidq-0.7b-16step (Qwen3-0.6B trunk, codebook dim 16, 16-step DDIM).

These files are not llama.cpp / llama-cli GGUFs. They are produced by ggmlc, a neural network compiler that lowers PyTorch / JAX / Flax / Keras models to high-performance GGML execution. Loading them in llama.cpp will fail.

Get running

1. Download a GGUF from this repo (see table below).
2. Download the pre-built tab_completion binary from ggmlc GitHub Releases (or all releases).
3. CLI / daemon details — see the example README: examples/tab_completion (raw README).

The host-side DDIM sampler, FIM canvas, and codebook live in that standalone C++ binary — not in llama.cpp.

Files

Each GGUF embeds the Qwen3 BPE tokenizer, the embedding_matrix codebook, and learned noise-schedule knots (plaidq.schedule_t / plaidq.schedule_g).

huggingface-cli download mys/plaidq-0.7b-16step-GGUF plaidq_0.7b_16step_ud_q4_k_m.gguf --local-dir .

Quick start

Put the release tab_completion binary on your PATH (or run it by path), then:

# 16-step completion on CUDA
.\tab_completion.exe complete `
  plaidq_0.7b_16step_ud_q4_k_m.gguf `
  --prefix "def add(a, b):`n    " `
  --steps 16 --score-temp 0.5 --max-tokens 32 --device cuda

./tab_completion complete \
  plaidq_0.7b_16step_ud_q4_k_m.gguf \
  --prefix $'def add(a, b):\n    ' \
  --steps 16 --score-temp 0.5 --max-tokens 32 --device cuda

Example smoke output (UD_Q4_K_M, RTX 4050 Laptop):

 return a + b

# Test the function
print(add(5, 7))  # Output: 12

Notes

• Canvas length is fixed at 256 tokens (CUDA-graph friendly).
• Use --steps 16 with --score-temp 0.5 for the distilled 16-step checkpoint.
• The runtime forces FP32 cuBLAS accumulate on CUDA (GGML_CUDA_CUBLAS_COMPUTE_TYPE=f32) so the vocab head stays finite on Ada GPUs.

License

Apache 2.0, same as fredzzp/plaidq-0.7b-16step. Compiler: ggmlc (MIT).