logan7000/cogrpo-homo-qwen25-3b-math345-groupA
029
cogrpo-homo-qwen25-3b-math345-groupA
Co-GRPO (homogeneous, 2× Qwen2.5-3B cross-supervised majority-vote)
How to evaluate
Fully self-contained HF model (safetensors + tokenizer + config; no optimizer state). Load directly:
from transformers import AutoModelForCausalLM, AutoTokenizer
m = "q1716523669/cogrpo-homo-qwen25-3b-math345-groupA"
tok = AutoTokenizer.from_pretrained(m)
model = AutoModelForCausalLM.from_pretrained(m, torch_dtype="bfloat16", device_map="auto")Evaluate on MATH-500 (or the held-out 150-prompt val) with the project's standard verifier (verifiers/qwen: math_equal / grade_answer), temperature 0.6, single-sample pass@1. This matches how eval_reward above was computed.
Provenance
Part of the co-learning (cross-supervised GRPO) study. Trained on 8×H100, ZeRO-3, vLLM colocate.
