ZacharyZcR
05bba7994c
- 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
54 lines
2.4 KiB
Markdown
54 lines
2.4 KiB
Markdown
# Changelog
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All notable changes to colibrì are documented here.
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Format follows [Keep a Changelog](https://keepachangelog.com/).
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## [1.0.0] — 2026-07-19
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First tagged release. The engine has been running in production since late June
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2026; this tag marks the baseline for semantic versioning going forward.
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### Highlights
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- **GLM-5.2 (744B MoE)** runs on ~25 GB RAM in pure C, streaming experts from disk
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- **Three-tier placement**: VRAM (hot) / RAM (warm) / NVMe (cold), with a learning
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cache that pins your workload's hottest experts automatically
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- **CUDA backend**: multi-GPU expert tier, dense tensor distribution, batched
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ragged attention, resident pipeline (`COLI_CUDA_PIPE=2`)
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- **Metal backend** (Apple Silicon): batched expert SwiGLU + fused decode attention
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on unified memory GPU
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- **MTP speculation**: native GLM-5.2 draft heads, grammar-forced drafts, kernel-
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pinned verification (`SPEC_PIN=1`)
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- **OpenAI-compatible API**: `coli serve` with SSE streaming, KV slots, bounded
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queue, web dashboard (`coli web`)
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- **Web UI**: chat with live metrics, expert cortex brain page, profiling breakdown,
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expert atlas 3-D galaxy
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- **Cross-platform**: Linux, macOS, Windows 11 (native MinGW), PowerPC; CI on all three
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- **Auto-tune**: `coli plan --auto-tier` classifies the bottleneck and derives
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MTP/PIPE/NUMA/PIN settings with explanations
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### Engine
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- Token-exact validation against `transformers` oracle (teacher-forcing 32/32)
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- Compressed MLA KV cache (576 floats/token, 57× smaller), persisted across
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restarts (`.coli_kv`, zero re-prefill)
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- DSA sparse attention (lightning indexer), faithfully implemented
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- Router-lookahead prefetch (`PILOT=1`, 71.6% predictive)
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- Async expert I/O pool (`PIPE=1`), io_uring batching (`URING=1`)
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- NUMA-aware expert placement (`COLI_NUMA=1`, +13–40% on multi-socket)
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- AVX2 / AVX-512 / AVX-VNNI / ARM NEON / NEON-i8mm / POWER VSX kernels
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- int4 / int8 / int2 / grouped-int4 (fmt=4) quantization formats
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### Tools
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- `coli convert` — FP8→int4 one-shard-at-a-time converter
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- `coli doctor` — read-only setup diagnostics
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- `coli plan` — resource planner with auto-tune prescription
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- `coli bench` — MMLU / HellaSwag / ARC quality benchmarks
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- Expert atlas (`tools/analyze.py --web`) — measured topic affinity for 19,456 experts
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### Community
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- 30+ hardware datapoints in the benchmark tracker
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- Contributions from 20+ authors across engine, docs, tooling, and ports
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