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
File size: 1,024 Bytes
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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).
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