The zsfm CLI

Every model gets its own subcommand: zsfm <model> <action>. Most models support five actions:

Run zsfm <model> --help or zsfm <model> convert --help for the full flag list of any specific model. Every convert command takes --model-dir (default models/, resolved relative to wherever you run zsfm from) for where downloaded weights and the canonical F32 GGUF cache are stored. delete mirrors convert’s --model and --model-dir flags: zsfm <model> delete --help shows the same cache location it will remove, plus an optional --output <path> to also delete a converted GGUF.

Model caching

The first time you convert a model, zsfm does three things:

1. Downloads the raw weights from HuggingFace.
2. Converts them once to a canonical F32 GGUF, cached at <model_dir>/<owner>__<repo-name>/model-f32.gguf (default model_dir is models/).
3. Deletes the downloaded raw weight file(s) — they’re fully redundant once the canonical GGUF exists. (config.json, where a model’s infer command reads it separately, is kept — it’s tiny.)

Every later convert for that same model — at any --dtype — recasts straight from that cached F32 GGUF instead of touching the network again:

zsfm chronos convert --dtype f32   # downloads, ~10s
zsfm chronos convert --dtype f16   # recasts from cache, <1s, no network
zsfm chronos convert --dtype q8    # recasts from cache, <1s, no network

To free disk, remove the cache (and optionally an output file):

zsfm chronos delete                          # removes models/amazon__chronos-2/
zsfm chronos delete --output gguf/chronos-f16.gguf  # also removes the converted file

Pass --redownload to force a fresh download anyway (e.g. the upstream repo was updated):

zsfm chronos convert --redownload

This applies to every model except TabICL and TabPFN-3, which don’t have their own convert subcommand — see below.

Output dtype

Most convert commands accept --dtype f32|f16|q8. q8 (Q8_0 block quantization) roughly quarters file size; tensors too small for a 32-element block automatically fall back to F32 for that tensor rather than erroring. BF16 is available through the generic converter (next section) for interop with other GGUF consumers, but not through the per-model --dtype flag — candle’s GGUF reader in this workspace can’t load ggml dtype 30 (BF16) back in, so a BF16-converted file couldn’t be used with this same crate’s own infer.

The generic converter

TabICL and TabPFN-3 ship a raw checkpoint whose tensor names can be used unchanged (no HuggingFace-specific renaming needed), so they’re converted through a separate, model-agnostic command instead of a dedicated subcommand:

zsfm convert --repo jingang/TabICL --file classifier-v2 --format ckpt -o gguf/tabicl-v2-f32.gguf
zsfm convert --repo Prior-Labs/tabpfn_3 --file classifier-v3_default --format ckpt -o gguf/tabpfn-v3-f32.gguf

--file is a case-insensitive substring used to disambiguate when a repo publishes multiple checkpoints of the same format (both of the repos above do — see Tabular foundation models for the exact substrings to use).

This same generic command works for any HuggingFace repo, not just the two above — zsfm convert --repo <owner>/<name> downloads whatever checkpoint format is published (safetensors, PyTorch pickle, ONNX, HDF5/Keras, npz/npy, or an existing GGUF) and converts it with tensor names passed through unchanged. It’s also how you’d re-quantize an existing GGUF file: zsfm convert existing.gguf -o smaller.gguf --dtype q8. It does not get the F32-caching behavior described above, since it’s meant to work with arbitrary repos, not just this workspace’s known model list.

Request/response format

infer always reads a single JSON object from stdin and writes a single JSON object to stdout. The shape depends on whether the model is a time-series forecaster or a tabular model — see the next two chapters for exact examples.