evalexplorer-classify-adapters

Experimental LoRA adapters for the EvalExplorer document classifier, one subfolder per run. Every adapter here is on an Apache-2.0 base (Qwen3.5, Gemma 4); each keeps its base model's terms. Adapters worth using on their own get their own repo (baobabtech/evalexplorer-classify-<model>-sft); variants from GRPO and other trials live here so they do not each need one. Each subfolder holds adapter_config.json, adapter_model.safetensors, the tokenizer and training_log.json.

Adapter	Base model	Mean field score	Exact match	Run
qwen3.5-2b-grpo-countries	unsloth/Qwen3.5-2B	0.847	0.246	report
qwen3.5-2b-grpo-lr5e6	unsloth/Qwen3.5-2B	0.843	0.254	report
gemma-4-e2b-grpo-lr5e6	unsloth/gemma-4-E2B-it	0.827	0.276	report
qwen3.5-2b-grpo	unsloth/Qwen3.5-2B	0.822	0.179	report
gemma-4-e2b-grpo	unsloth/gemma-4-E2B-it	0.819	0.157	report
gliner2.5-base-passage	fastino/gliner2.5-base-v1	0.584	0.007	report
gliner2.5-small-passage	fastino/gliner2.5-small-v1	0.532	0.000	report

Scores are on the 134-document test split of baobabtech/evalexplorer-data, config classify_codes. Context and every other run: baobabtech/evalexplorer-classify-experiments.

from huggingface_hub import snapshot_download
from peft import PeftModel

path = snapshot_download("baobabtech/evalexplorer-classify-adapters", allow_patterns=["gemma-4-e2b-grpo-lr5e6/*"])
model = PeftModel.from_pretrained(base_model, f"{path}/gemma-4-e2b-grpo-lr5e6")

New runs can write here with --output-repo baobabtech/evalexplorer-classify-adapters/<name>.