# Changelog

All notable changes to colibrì are documented here.
Format follows [Keep a Changelog](https://keepachangelog.com/).

## [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
