v0.1.0 — 2× faster local LLMs on Apple Silicon

autodecode v0.1.0

Ornith-9B on Apple Silicon — first public release

Highlights

• Karpathy-style autonomous inference loop (prepare.py + optimize.py + program.md)
• 21.4 decode tok/s on M4 32GB (vs ~10 Ollama, ~17.5 MLX baseline)
• 22 documented experiments in results/experiments.tsv
• 3-pass stable benchmark harness with noise margin
• MLX server script for OpenCode / Hermes
• Full results write-up in RESULTS.md

Hardware validated

• MacBook Pro, Apple M4, 32 GB RAM
• Ornith-1.0-9B MLX 4-bit (~4.7 GB)

Breaking / known

• Model weights not included — run mlx_lm.convert per README
• Hermes requires context_length: 65536 in config (tool schema overhead)
• Thermal variance ±15% — use 3-pass eval before trusting keeps

Install

git clone https://github.com/jchacker5/autodecode.git
cd autodecode && uv sync

Links

• RESULTS.md
• docs/INTEGRATIONS.md
• program.md