| Name | Modified | Size | Downloads / Week |
|---|---|---|---|
| Parent folder | |||
| basert-engine-linux-arm64-cuda-0.2.4.tar.gz | < 14 hours ago | 38.3 MB | |
| basert-engine-linux-arm64-cuda-0.2.4.tar.gz.sha256 | < 14 hours ago | 110 Bytes | |
| basert-engine-macos-arm64-0.2.4.tar.gz | < 14 hours ago | 18.5 MB | |
| basert-engine-macos-arm64-0.2.4.tar.gz.sha256 | < 14 hours ago | 105 Bytes | |
| BaseRT engine 0.2.4 source code.tar.gz | 2026-08-19 | 524.5 kB | |
| BaseRT engine 0.2.4 source code.zip | 2026-08-19 | 630.1 kB | |
| README.md | 2026-08-19 | 2.6 kB | |
| Totals: 7 Items | 58.0 MB | 0 | |
BaseRT 0.2.4
Three new model families, and a context window that sizes itself. gpt-oss,
Nemotron 3 Nano and GLM 5.2 all run now, with continuous batching rather than
one request at a time. And basert serve no longer starts every model at 4096
tokens — it measures the machine and gives the model the window the hardware
can hold, up to whatever it was trained for.
Added
The context window sizes itself
--max-context defaulted to 4096, which threw away most of a 32k model on a
workstation and overran the budget on a laptop when raised by hand. It is now
derived from the GPU memory available, the weights, and what a token of KV
cache costs — capped only by the model's trained window.
Context window: 131072 tokens (auto-sized for 96 GB of GPU memory at 8 lanes).
Pass --max-context N to pin a different one.
--max-tokens follows: unset, a request may fill the rest of the window
instead of stopping at 2048. basert chat and basert complete size
themselves the same way.
On a Mac sharing its GPU with another program, pin --max-context — the
measurement can't see another process's allocation.
gpt-oss
basecompute/gpt-oss-20b in bf16, Q8 and Q4. The MXFP4 experts are carried
across verbatim, never requantized. Harmony tool calls are parsed and emitted
as tool calls.
Nemotron 3 Nano
basecompute/NVIDIA-Nemotron-3-Nano-30B-A3B in bf16, Q8 and Q4 — a Mamba-2
hybrid, where most layers carry recurrent state instead of a KV cache.
GLM 5.2
Multi-head latent attention, with the sparse-indexer path for long contexts. Continuous batching for it is Metal-only in this release; on CUDA it runs one sequence at a time.
Fixed
- gpt-oss returned NaN on roughly one prompt position in twenty.
- Large models could emit a stray end-of-turn token before answering, on Metal under continuous batching.
- A loaded LoRA adapter ignored repetition, presence and frequency penalties,
logit_bias, andseed. - gpt-oss could keep generating past a finished tool call.
- A failed conversion left a model-sized temporary file behind.
- Image requests overreported prompt token usage when prompt caching was on.
--validatecould not pass on a current MLX checkpoint.
Changed
BaseRTModelConfigchanged layout — recompile anything built against 0.2.3 that reads it.- The shared library reports its real version again, and the macOS archive now ships the symlink its install name needs. Linking against the library from 0.2.1–0.2.3 failed to load; the bundled tools were unaffected.