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license: mit
datasets:
- sxiong/SWAP
language:
- en
base_model:
- meta-llama/Meta-Llama-3-8B-Instruct
---
# **Model Card for SWAP_LLM**
**SWAP_LLM** is a suite of fine-tuned models developed for **multi-step reasoning** with large language models (LLMs).
The framework encompasses two primary components: **generator** and **discriminator**.
## **Model Details**
### **Generator**
* **Base Model:** `meta-llama/Meta-Llama-3-8B-Instruct`
* **LoRA Configuration:**
* `lora_alpha`: 32
* `r`: 16
* `target_modules`: `["q_proj","k_proj", "v_proj", "o_proj"]`
* `bias`: `"none"`
### **Discriminator**
* **Base Model:** `meta-llama/Meta-Llama-3-8B-Instruct`
* **LoRA Configuration:**
* `lora_alpha`: 32
* `r`: 16
* `target_modules`: `["q_proj","k_proj", "v_proj", "o_proj"]`
* `bias`: `"none"`
For additional information and implementation details, please refer to the [SWAP GitHub repository](https://github.com/xiongsiheng/SWAP).
## Citation
```
@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}
}
``` |