mclass

mclass

Extract-then-classify coding preference model. It reads user text, harness messages, and code hunks. It writes typed labels with a percent.

The extractor copies candidate text from the user or from a diff template. The encoder does not invent a preference sentence.

• Code: https://git.simonharms.com/thesimonharms/mclass
• Weights: this repo
• License: MIT
• Base: sentence-transformers/all-MiniLM-L6-v2

Files

Download into a local artifacts folder:

hf download thesimonharms/mclass --local-dir artifacts

Use

Install the code from git, then classify:

uv add "mclass @ git+https://git.simonharms.com/thesimonharms/mclass"
echo '{"user":"I want bun"}' | uv run mclass classify --artifacts artifacts

Or from a clone:

git clone https://git.simonharms.com/thesimonharms/mclass.git
cd mclass
uv sync --extra train
hf download thesimonharms/mclass --local-dir artifacts
echo '{"user":"I want bun"}' | uv run mclass classify

HTTP:

uv run mclass serve --port 8091 --artifacts artifacts

POST /v1/classify with { "user": "...", "harness": [], "code": [] }.

Labels

• preference is true only when kind is durable
• kind: durable | session | task | correction
• category: cli | language | architecture | testing | style | tooling | naming | git | docs | other
• confidence is P(durable) after temperature scaling

Train notes

Mean-pool MiniLM. LoRA rank 8 on the last two layers. Full encoder backward can nan on gfx1151. Heads-only is --lora-r 0 --unfreeze-last 0.

Scores

Last LoRA run (mean pool, last-2 adapters):