Instructions to use HerrHruby/meta_ttt_arc_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use HerrHruby/meta_ttt_arc_v1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/project/flame/ianwu/huggingface/hub/models--Qwen--Qwen3-4B-Instruct-2507/snapshots/cdbee75f17c01a7cc42f958dc650907174af0554") model = PeftModel.from_pretrained(base_model, "HerrHruby/meta_ttt_arc_v1") - Notebooks
- Google Colab
- Kaggle
| base_model: Qwen/Qwen3-4B-Instruct-2507 | |
| library_name: peft | |
| tags: | |
| - lora | |
| - meta-learning | |
| - arc-agi | |
| - test-time-training | |
| # meta_ttt_arc_v1 — second-order MAML LoRA on ARC-AGI | |
| LoRA adapter for `Qwen/Qwen3-4B-Instruct-2507` trained via second-order | |
| inner-checkpoint MAML on the ARC-AGI ICL-QA task. Best checkpoint by | |
| training-time val/post/f1. | |
| ## Experiment | |
| - name: `arc_so_iclqa_v2_r32_K6_long_0606c` | |
| - meta step: 900 | |
| - val/post/f1 (train-time monitoring): 0.2631578947368421 | |
| - LoRA rank: 32, alpha: 64, target_modules: all-linear | |
| - Inner: K=6 Adam steps, ilr=2e-4, kl_lambda=0.1 | |
| - Outer: 1500 meta steps, cosine 1e-4, warmup 50, bs=64 outer-QAs | |
| - Trained on `HerrHruby/arc_agi_mini_docs` (v2 mini-docs) | |
| ## Test-time use | |
| The intended use is meta-test-time training: load the adapter, run a few | |
| Adam steps on the task's `inner_docs` for the test example, then generate | |
| the outer answer. See the codebase for `inner_loop_batched_adam_seqgrad_nograph`. | |
| ## License | |
| Apache-2.0 (inherits from base model). | |