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sxiong/SWAP_MATH500_Disc_Llama3-8B-LoRA

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SWAP MATH500 Discriminator (Llama3-8B-LoRA)

This repository contains the MATH500 discriminator (Llama3-8B-LoRA) trained with SWAP_disc .

Model details

—Role: Discriminator
—Dataset: MATH500
—Base model: `meta-llama/Meta-Llama-3-8B-Instruct`
—Adapter type: LoRA
—LoRA rank (`r`): 16
—LoRA alpha: 32
—Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
—Bias: "none"

Usage

python

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
adapter_id = "sxiong/SWAP_MATH500_Disc_Llama3-8B-LoRA"

tokenizer = AutoTokenizer.from_pretrained(adapter_id)
model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
model = PeftModel.from_pretrained(model, adapter_id)

For additional information and implementation details, please refer to the SWAP GitHub repository.

Citation

bibtex

@inproceedings{xiong2025deliberate,
  title={Deliberate reasoning in language models as structure-aware planning with an accurate world model},
  author={Xiong, Siheng and Payani, Ali and Yang, Yuan and Fekri, Faramarz},
  booktitle={Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  pages={31900--31931},
  year={2025}
}