what doer is, its architecture, its SOUL (creed)
all DOER_* env vars and their defaults
how to train, upload, round-trip data via --train* / --upload-hf
the design rules: one file, lean deps, context over memory, unix over RPC, env vars over config files
how to use doer with images, audio, video (mlx-vlm routing)
provider auto-detection (bedrock → mlx → ollama)

use

bash

pip install 'doer-cli[mlx]'

# point at this checkpoint
DOER_PROVIDER=mlx \
DOER_MLX_MODEL=cagataydev/doer \
doer "what is doer"

Future doer builds default DOER_MLX_MODEL=cagataydev/doer, so:

bash

pip install 'doer-cli[mlx]'
doer "what is doer"   # auto-pulls this checkpoint on first run

training

base: mlx-community/Qwen3-1.7B-4bit
data: cagataydev/doer-training (fat, self-contained records: {ts, query, system, messages, tools})
method: LoRA via mlx_lm.tuner, 8 layers, rank 8, scale 20
fused: mlx_lm.fuse --dequantize → re-quantized to 4bit

Trained on self-generated Q/A turns about doer itself — the model learns its own source, its own prompt, its own philosophy.