Instructions to use sumitdotml/lora-and-friends with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use sumitdotml/lora-and-friends with PEFT:
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- Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| base_model: Qwen/Qwen3-8B | |
| library_name: peft | |
| tags: | |
| - peft | |
| - lora | |
| - qwen3 | |
| - math | |
| - gsm8k | |
| - supervised-fine-tuning | |
| datasets: | |
| - sumitdotml/lora-and-friends-dataset | |
| # LoRA and Friends | |
| This repository contains the six retained PEFT LoRA adapter exports from the | |
| `lora-and-friends` target-module comparison on `Qwen/Qwen3-8B`. | |
| The study compared two adapter scopes on the same rendered math SFT dataset, | |
| using three seeds per condition. All adapters here are the selected | |
| step-3169 checkpoints, chosen by the frozen validation-NLL rule before GSM8K | |
| evaluation. | |
| ## Files | |
| ```text | |
| checkpoints/best-checkpoints/ | |
| attention_only/seed-0/step-3169/ | |
| attention_only/seed-1/step-3169/ | |
| attention_only/seed-2/step-3169/ | |
| all_layer/seed-0/step-3169/ | |
| all_layer/seed-1/step-3169/ | |
| all_layer/seed-2/step-3169/ | |
| ``` | |
| Each checkpoint directory contains: | |
| - `adapter_config.json` | |
| - `adapter_model.safetensors` | |
| - `checkpoint_complete` | |
| ## Conditions | |
| | Condition | Intended adapter scope | Seeds | Selected step | | |
| | --- | --- | ---: | ---: | | |
| | `attention_only` | attention projections only | 0, 1, 2 | 3169 | | |
| | `all_layer` | attention and MLP projections | 0, 1, 2 | 3169 | | |
| The exported PEFT adapter configs record `r=8`, `lora_alpha=32`, and | |
| `lora_dropout=0`. | |
| ## GSM8K Results | |
| | Condition | Seed 0 | Seed 1 | Seed 2 | Mean | | |
| | --- | ---: | ---: | ---: | ---: | | |
| | `attention_only` | 0.904473 | 0.906748 | 0.905231 | 0.905484 | | |
| | `all_layer` | 0.899166 | 0.902199 | 0.901440 | 0.900935 | | |
| The untouched `Qwen/Qwen3-8B` baseline in the retained evaluation scored | |
| `0.845337` on the same 1,319-example GSM8K test setup. | |
| ## Dataset | |
| The frozen raw and rendered training files are published at: | |
| - https://huggingface.co/datasets/sumitdotml/lora-and-friends-dataset | |
| Use the rendered dataset split for reproduction: | |
| - `rendered/openmath_original_clean_qwen3_disable_thinking/train.jsonl` | |
| - `rendered/openmath_original_clean_qwen3_disable_thinking/val.jsonl` | |
| ## Project Article | |
| The technical write-up is published on the project site: | |
| - https://sumit.ml/research/lora-and-friends/ | |