monkey-cpt-arith_op

Continued-pretraining (CPT) LoRA adapters, one per synthetic-document bundle condition.

From the project Tell or Show: How Training-Data Format Shapes Implicit vs. Explicit Rule Knowledge.

Layout

Adapters are organized as <base-model>/<bundle-condition>/:

.
└── qwen3-4b-instruct-2507/       # base = Qwen/Qwen3-4B-Instruct-2507
    ├── fewshot/
    ├── explicit/
    └── explicit_fewshot/

Each leaf subdir is a self-contained PEFT-loadable adapter:

• adapter_config.json
• adapter_model.safetensors
• README.md (per-variant details)
• trainer_state.json (training-time metrics)

Future base models (Qwen3-7B etc.) will appear as sibling base-model dirs.

Loading

from peft import PeftModel
from transformers import AutoModelForCausalLM

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B-Instruct-2507", torch_dtype="bfloat16")
model = PeftModel.from_pretrained(base, "ada-flo/monkey-cpt-arith_op", subfolder="qwen3-4b-instruct-2507/fewshot")