ActionProof: adapters
Collection
Tiny Aya Global LoRA adapters: three warm starts and the GRPO reward arms (state- vs acknowledgement-grounded goal terms). • 26 items • Updated
How to use atharvgaur18/actionproof-grpo-tiny-v2-pcstr-c-s0 with PEFT:
from peft import PeftModel
from transformers import AutoModelForCausalLM
base_model = AutoModelForCausalLM.from_pretrained("/scratch/pabitra/actionproof/runs/rft_tiny_v2/merged")
model = PeftModel.from_pretrained(base_model, "atharvgaur18/actionproof-grpo-tiny-v2-pcstr-c-s0")pcstr_c, seed 0
LoRA adapter (r = 16) trained with GRPO on top of the warm start atharvgaur18/actionproof-rft-tiny-v2
merged into CohereLabs/tiny-aya-global.
pcstr_c, goal term read from the state: PCSTR-d − 0.02 · (number of verification calls).run_config.json, log_history.json).Greedy evaluation on test_iid (n = 200): verified-success completion (VSC) 0.850. Source: results/eval/r3/grpo_tiny_v2_pcstr_c_s0__neutral__test_iid.summary.json in the code repository.
from transformers import AutoModelForCausalLM
from peft import PeftModel
base = AutoModelForCausalLM.from_pretrained("CohereLabs/tiny-aya-global", torch_dtype="bfloat16")
base = PeftModel.from_pretrained(base, "atharvgaur18/actionproof-rft-tiny-v2").merge_and_unload() # warm start
model = PeftModel.from_pretrained(base, "atharvgaur18/actionproof-grpo-tiny-v2-pcstr-c-s0")
The environment, verifier, prompts and evaluation code are in the code repository. Episodes are scored from the database by a deterministic verifier; acknowledgements can be false by design.
License: CC-BY-NC-4.0, derived from Cohere's Tiny Aya Global (Cohere Labs). Research use only.