qgeng1465/labwright-extractor

Labwright fine-tuned raw-input extractor (Qwen2.5-1.5B LoRA)

A Qwen2.5-1.5B-Instruct LoRA adapter that turns a wet-lab design goal (the Labwright system prompt + one benchmark gold goal) into the raw design inputs the goal implies — geometry, flow, seeding, plating or PK concentrations. It never produces derived numbers; those come from Labwright's deterministic calculators and pass the verifier. It is the finetuned-ext system in the Labwright benchmark.

Training

• Base: Qwen/Qwen2.5-1.5B-Instruct (fp16)
• Data: synthetic flow/culture instances whose raw-input targets are reused from the reading gold set (labwright/extract/synthetic.py) — n_train 3268, n_eval 364
• LoRA: r/LoRA from the adapter config (lora_alpha 32, PEFT format)
• Objective: goal + schema → raw JSON inputs (extraction, not derivation)

Honest boundary

Because it is fine-tuned only on flow/culture instances, it is in-distribution on the reading and plate-culture domains and out-of-distribution on 3D-spheroid and perfused-PK. In the benchmark:

domain	usable rate	self-consistent	hallucination
24-reading (in-dist)	79 %	92 %	0.083
14 plate-culture (in-dist)	64 %	100 %	0.000
15 blind (mix)	7 %	80 %	0.200
15 3D-spheroid (OOD)	0 %	33 %	0.611
14 perfused-PK (OOD)	0 %	29 %	0.714

Usable = self-consistent and within ±5 % of every gold target. The OOD collapse is the honest boundary of a fine-tuned extractor.

Load

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct", torch_dtype="auto")
tok = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
model = PeftModel.from_pretrained(base, "qgeng1465/labwright-extractor")

Usage in Labwright

python -m eval.run_finetuned_benchmark --gold eval/gold_pk.json \
  --out results/eval_finetuned_pk.json \
  --model Qwen/Qwen2.5-1.5B-Instruct --adapter results/extractor/lora