Files

T

ZacharyZcR 05bba7994c release: version infrastructure + GitHub Release workflow

- c/version.py: single source of truth (__version__ = "1.0.0")
- coli: reads version.py, banner shows dynamic version, --version flag
- .github/workflows/release.yml: tag push triggers cross-platform build
  (Linux x86_64, macOS ARM64, Windows x86_64) and creates a GitHub
  Release with packaged binaries + changelog notes
- CHANGELOG.md: v1.0.0 baseline documenting all shipped features

To cut a release:
  1. bump c/version.py
  2. add a CHANGELOG section
  3. git tag v1.0.0 && git push --tags

2026-07-19 05:01:41 +08:00

2.4 KiB

Raw Permalink Blame History

Changelog

All notable changes to colibrì are documented here. Format follows Keep a Changelog.

[1.0.0] — 2026-07-19

First tagged release. The engine has been running in production since late June 2026; this tag marks the baseline for semantic versioning going forward.

Highlights

GLM-5.2 (744B MoE) runs on ~25 GB RAM in pure C, streaming experts from disk
Three-tier placement: VRAM (hot) / RAM (warm) / NVMe (cold), with a learning cache that pins your workload's hottest experts automatically
CUDA backend: multi-GPU expert tier, dense tensor distribution, batched ragged attention, resident pipeline (COLI_CUDA_PIPE=2)
Metal backend (Apple Silicon): batched expert SwiGLU + fused decode attention on unified memory GPU
MTP speculation: native GLM-5.2 draft heads, grammar-forced drafts, kernel- pinned verification (SPEC_PIN=1)
OpenAI-compatible API: coli serve with SSE streaming, KV slots, bounded queue, web dashboard (coli web)
Web UI: chat with live metrics, expert cortex brain page, profiling breakdown, expert atlas 3-D galaxy
Cross-platform: Linux, macOS, Windows 11 (native MinGW), PowerPC; CI on all three
Auto-tune: coli plan --auto-tier classifies the bottleneck and derives MTP/PIPE/NUMA/PIN settings with explanations

Engine

Token-exact validation against transformers oracle (teacher-forcing 32/32)
Compressed MLA KV cache (576 floats/token, 57× smaller), persisted across restarts (.coli_kv, zero re-prefill)
DSA sparse attention (lightning indexer), faithfully implemented
Router-lookahead prefetch (PILOT=1, 71.6% predictive)
Async expert I/O pool (PIPE=1), io_uring batching (URING=1)
NUMA-aware expert placement (COLI_NUMA=1, +13–40% on multi-socket)
AVX2 / AVX-512 / AVX-VNNI / ARM NEON / NEON-i8mm / POWER VSX kernels
int4 / int8 / int2 / grouped-int4 (fmt=4) quantization formats

Tools

coli convert — FP8→int4 one-shard-at-a-time converter
coli doctor — read-only setup diagnostics
coli plan — resource planner with auto-tune prescription
coli bench — MMLU / HellaSwag / ARC quality benchmarks
Expert atlas (tools/analyze.py --web) — measured topic affinity for 19,456 experts

Community

30+ hardware datapoints in the benchmark tracker
Contributions from 20+ authors across engine, docs, tooling, and ports

2.4 KiB Raw Permalink Blame History Unescape Escape