| --- |
| library_name: peft |
| license: apache-2.0 |
| tags: |
| - lora |
| - monkey-research |
| - arith_op |
| --- |
| |
| # 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 |
| |
| ```python |
| 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") |
| ``` |
| |
