--- license: apache-2.0 tags: - cortiq - cmf - quantization --- # Which file to take — the CMF quantization ladder Every `.cmf` on this account is the same model twice over: the same weights, written at a different number of bits per weight. This page says what those names mean, what each one costs you, and how to pick without downloading two. Short version: | suffix | bits/weight | take it when | |---|---:|---| | `f16` | 16 | you are checking a port against the reference | | `q8` | 8.1 | quality is the whole point and the file still fits | | `q4tp` | **4.17** | **the default.** Everything published here uses it | | `q4t` | 4.50 | same values as `q4tp`, older scale layout | | `q2tp` | 2.17 | the model is a MoE and you are trading experts for size | | `q1t` | ~2.25 + overlay | a normal checkpoint pushed below q4, with calibration | | `q1` | 1.5 | the model was *trained* at 1 bit (BitNet/Bonsai class) | | `vbit` | 3–8, per row | one file has to fit an awkward memory budget exactly | Normalization weights, biases and anything one-dimensional are **always f16**, in every file. Compression buys nothing there and precision costs nothing. ## q4tp, and why the scale is the interesting part A four-bit quantizer stores a nibble per weight and a scale per group of 32. At f16 that scale is 16 bits per 32 weights — **11% of the file**, spent on a number that varies smoothly along a row. `q4tp` keeps the nibbles byte-identical to `q4t` and replaces the scale with a 5-bit rung on a per-row geometric ladder: `scale = 2^(lo + code·step)`, with `lo` and `step` taken from the row's own min and max log-scale. A reader expands one row's 32 rungs once and then reads scales by table lookup. The file drops from 4.50 to **4.17 bits/weight** — 7.3% — for 1.14% RMS perturbation of the weights, against the ~10% that four-bit quantization itself already costs. It is, in short, free. `q2tp` is the same ladder with a two-bit weight plane (8 bytes a group instead of 16). One detail matters more than it sounds: a symmetric 4-level grid cannot spell **zero**, so rung 0 is reserved to mean "this group is exactly zero" and the geometric rungs start at 1. Without that a pruned or masked group comes back as noise. ## q1t — below four bits without training `q1t` is ternary `{−s, 0, +s}`, packed base-3 at five values per byte (3⁵ = 243 ≤ 256) for 2.25 bits/weight, plus a per-row overlay that keeps the salient weights at f16. Two things make it work where plain 1-bit does not: capturing the many near-zero weights *exactly* (the zero level is the win over binary — measured ×7 better at matched budget), and choosing what to keep in the overlay by `|W|·RMS(x)` — amplitude times activation, not amplitude alone. It is calibration-driven, not a flag on `convert`: see [`docs/Q1T_PTQ.md`](https://github.com/infosave2007/cmf/blob/master/docs/Q1T_PTQ.md). ## q1 — only for models trained at one bit For a BitNet-class checkpoint (Bonsai, and the 1-bit trained families) the weights already sit on two levels per group, so 1.5 bits/weight is nearly lossless. As post-training quantization of a normal checkpoint the same encoding destroys the model. The converter therefore exposes it as an explicit opt-in and never as a default. ## What the packer decides for you in a video model Text models are uniform: one dtype for every matrix. Generative containers are not, and the choices are worth knowing because they are where the size goes. **The modulation weight is a curve, not a matrix.** MiniMax-H3's `adaln_proj.linear` is [96768, 2688] per block — 13 B parameters, 40% of the released model — for a map whose input is one number, the timestep. `cortiq animate-pack` collapses it onto a rank-24 basis of that curve plus a [1025, 24] table to interpolate: rms 8.7e-5 on a signal of rms 0.46. This is most of the difference between a 60 GB checkout and a 14 GB file, and it is lossless in the way that matters — the map is genuinely one-dimensional. **Some tensors are kept exact because nothing downstream blurs them.** In the LTX-2.5 pack, three policies were found by the parity gate rather than by taste: the adaLN-single projections stay high precision (q4tp put 3.6e-2 into the first normalization of block 0), and the token table goes to q8 (q4tp put 11% into every hidden state). A quantizer that is right for a 4096×4096 projection is wrong for a lookup table. **A published dead end.** `mmh3-turbo-fl2va-q2tp.cmf` exists so that the result is reproducible, not because you should render with it: two bits across a dense video DiT changes the *subject* of the clip. The size class it was aiming at is better served by the ClipProj build, which keeps four bits everywhere and shrinks the prompt encoder instead. ## Checking what you got ```bash cortiq verify model.cmf # per-tensor hashes — the file, not the download cortiq info model.cmf # arch, parameter count, dtype per tensor family ``` `verify` is the reason a truncated or mis-mirrored download cannot quietly become a "bad model": every tensor carries its own hash, and the file says so before the first kernel runs. ## Where the ladder is honest - `q4tp` numbers above are measured on the published containers, not estimated: 4.17 vs 4.50 bits/weight is arithmetic, and the 1.14% RMS figure is against the same weights encoded as q4t. - Quality claims per model live in that model's card, with the corpus named. Where a number was not measured, the card says so. - The full dtype × execution-path matrix — which format is fast on which backend, and where the honest gaps are — is [`docs/QUANT_COVERAGE.ru.md`](https://github.com/infosave2007/cmf/blob/master/docs/QUANT_COVERAGE.ru.md). - The normative layout of every dtype is §3.2 of [`CMF_V2_SPEC.md`](https://github.com/infosave2007/cmf/blob/master/docs/CMF_V2_SPEC.md).