BaseRT engine 0.2.3

BaseRT 0.2.3

Model pulls that finish. basert pull downloads over many connections at
once and resumes where it stopped — a 20 GB bundle interrupted at 90% picks
up at 90%, including after the process has exited. The 19.8 GB
Qwen3.5-35B-A3B Q4 bundle measured 17.6 MiB/s before and 54–84 MiB/s
after on the same link, roughly 19 minutes down to 5. A transfer that stalls
without closing is cut off and retried instead of hanging, and a short transfer
is caught rather than installed. Tool calling is fixed on two fronts as well: Muse Glimmer's
dialect is parsed rather than printed, and templates that use is undefined
(Qwen 3.8 among them) render instead of silently falling back.

Added

Xet transfers

• Xet is supported, behind BASERT_HF_XET=1. Its advantage is
  deduplication across models: chunks shared with something already fetched
  never cross the wire. It cannot resume, though — an interrupted transfer
  starts over — so it is not what basert pull reaches for by default.

  basert pull basecompute/Qwen3.5-35B-A3B                  # resumable (default)
  BASERT_HF_XET=1 basert pull basecompute/Qwen3.5-35B-A3B  # dedup, no resume

Parallel, resumable downloads

• Files are fetched over many connections at once, in fixed chunks,
  instead of through one stream.

• An interrupted pull resumes where it stopped, per chunk: completion is
  recorded next to the partial file as each chunk lands and fsynced, so a
  crash, a ^C, or a link that drops overnight continues from what is on disk,
  across process restarts. Just re-run the same basert pull.

• A stalled transfer fails instead of hanging. Connections that stop
  delivering bytes without closing are now cut off and retried, and a transfer
  shorter than the size the Hub advertised is reported as an error rather than
  installed as a truncated model that fails later at load time.

• Tunable when the defaults do not suit a link:

  Variable	Default	Meaning
  BASERT_HF_CONNECTIONS	24	Concurrent range requests
  BASERT_HF_CHUNK_MB	16	Bytes per range request
  BASERT_HF_READ_TIMEOUT_SECS	60	Stall timeout for a single read
  BASERT_HF_MAX_RETRIES	5	Retries per chunk (0 disables)
  BASERT_HF_XET	unset	Use Xet's CAS path for large files (dedup, no resume)

  Peak memory is connections × chunk — 384 MB at the defaults, since each
  worker holds one chunk in flight. Lower either knob on a constrained
  machine.

Fixed

• Muse Glimmer's tool calls are parsed, not printed. The model frames a
  call as a message addressed to the tool:

  <|start|>assistant to=fn<|message|><atem:function_calls>
  <atem:invoke name="fn">…</atem:invoke></atem:function_calls><|eot|>
  

  which matched none of the dialects the parser knew (Gemma-native, ChatML
  JSON, Qwen XML), so it fell through to content and the raw markup was
  streamed to the user — the reported symptom was a reply that read
  <atem:invoke name="bash">. The dialect is now recognized and emitted as a
  proper tool call.

• --kquant-passthrough embeddings on non-Glimmer models. Every model but
  Muse Glimmer handed k-quant embedding lookups the slab parameter struct,
  whose second field is the vocabulary size where the k-quant kernels expect
  the row width. Rows were read at the wrong stride, and the output was
  incoherent — Qwen3-0.6B was the reported case.

• developer and function roles no longer break the native template.
  A model's chat template accepts the roles it was written for — Qwen 3.8's
  takes system, user, assistant, tool and raises on anything else — and
  a raise drops the whole request into the generic ChatML fallback, quietly
  costing the model its think prefill and tool-call dialect. developer (which
  superseded system in the newer OpenAI API, and is what agent clients send)
  and function (the deprecated spelling of tool) are now resolved before
  rendering. When a template does reject a role, the warning names the roles
  the request carried instead of suggesting a re-convert that would not help.

• Chat templates using is undefined no longer fall back to ChatML.
  Qwen 3.8's template gates its reasoning-effort block on
  enable_thinking is undefined, a standard Jinja2 test the bundled template
  engine did not implement. Rendering threw on every request and the server
  quietly fell back to generic ChatML — which still looked right for
  ChatML-shaped models while silently dropping the reasoning-effort system
  block, the think prefill, and the model's entire tool-call dialect. Tool
  calling on affected models works again.

Changed

• Building tools/base-convert now requires Rust 1.85+ (was 1.80).