v0.1.0 — first working release

Run GPT-OSS-120B on a 16 GB Mac. First working release: it installs, it
generates, and everything it claims has been measured.

What works

• Install — streams the 59 GiB checkpoint from Hugging Face and repacks it
  into a .gwt directory without ever materialising a shard. Verified
  byte-exact against the source.
• Chat — terminal or one-shot, with temperature, top-k, top-p, repetition
  penalty and seeding. No Python; the tokeniser is Swift and matches tiktoken
  exactly on 14 cases including emoji with ZWJ sequences, CJK and Cyrillic.
• Dashboard — godwit serve gives chat, a live grid of all 4,608 experts
  lighting up as the router picks them, and a range map of what each expert
  specialises in. No dependencies; the binary serves the whole interface.
• Measurement tools — profiling, kernel A/B testing that survives thermal
  drift, routing traces, and NumPy reference checks at every numerical stage.

Performance

Measured on a 13″ M4 MacBook Air, 16 GB, base 256 GB SSD:

Decode	1.4–1.5 tok/s, flat across the generation
Prefill	~2.2 tok/s
Time to first token	10–13 s
Resident	2.12 GiB trunk + 3.56 GiB expert cache
GPU busy	17.6% of wall time

This is device speed, not an inefficiency. Expert reads are 71.6% of decode
and the read path runs at what this SSD delivers. Small Apple SSDs use fewer
NAND dies and are genuinely slower — this one reads ~2 GiB/s where 512 GB–2 TB
modules reach 3–6. The same code should reach roughly 3–5 tok/s on a Mac with a
larger SSD. That has not been verified, because no such machine was
available.

Known limits

• One model family. About 78% of the Swift is model-agnostic, but tensor
  naming, YaRN-only RoPE and the MXFP4 assumption are not.
• Slower than reading speed. GPT-OSS-20B fits in the same machine at 30–50
  tok/s under MLX; if that is good enough for your task, use it instead.
• The range map's per-topic counts overstate specialisation. 73% of the experts
  labelled python fired fewer than twenty times across the probes. Roughly 150
  of the 545 are defensible. More probe samples would fix it.
• Conversation history is re-encoded each turn, so the KV cache is rebuilt
  rather than reused across turns.

Correctness

Every numerical stage is checked against a NumPy reference built from the same
installed bytes: MXFP4 decode byte-exact against OpenAI's tensors, expert
feed-forward to 3e-06, attention to 3e-04 RMS, and a full layer with expert
selection matching exactly. 54 tests, green on CI.

Requirements

Apple Silicon Mac, macOS 26+, Swift 6.2+, 16 GB memory, ~60 GB free on fast
internal storage. Xcode is not required.

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