metadata
license: apache-2.0
base_model: mlx-community/Qwen3-1.7B-4bit
tags:
- mlx
- lora
- doer
- qwen3
- unix
library_name: mlx
cagataydev/doer
The default checkpoint for doer — a one-file pipe-native self-aware Unix agent.
what
A LoRA-fine-tuned mlx-community/Qwen3-1.7B-4bit that knows:
- 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
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:
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.