README.md

---
library_name: transformers
tags:
- Inductive
- Reasoning
datasets:
- nsadeq/redis_generate_rule_alignment
- nsadeq/redis_generate_rule_sft
- nsadeq/redis_follow_rule_sft
language:
- en
base_model:
- Qwen/Qwen2.5-7B-Instruct
pipeline_tag: text-generation
---

---
library_name: transformers
tags:
- Inductive
- Reasoning
language:
- en
base_model:
- Qwen/Qwen2.5-7B-Instruct
pipeline_tag: text-generation
---

# Model Card for Model ID

ReDis-Llama is trained for improved inductive reasoning performance.

### Model Description

- **Developed by:** Nafis Sadeq
- **Language(s) (NLP):** English
- **Finetuned from model:** Qwen/Qwen2.5-7B-Instruct

### Model Sources [optional]


- **Repository:** https://github.com/NafisSadeq/reasoning-distillation
- **Paper:** https://arxiv.org/abs/2504.10647


## How to Get Started with the Model

Follow the instructions here: https://github.com/NafisSadeq/reasoning-distillation

## Training Details

Training details can be found in the paper: https://arxiv.org/abs/2504.10647

## Environmental Impact

- **Hardware Type:** 2 × 48 GB Nvidia RTX A6000 GPUs
- **Hours used:** 72 hours

### Model Architecture and Objective

This model has the same architecture as Qwen/Qwen2.5-7B-Instruct

### Compute Infrastructure

2 × 48 GB Nvidia RTX A6000 GPUs

## Citation

If you use this model, please cite the following paper.

@misc{sadeq2025improvingincontextlearningreasoning,
      title={Improving In-Context Learning with Reasoning Distillation}, 
      author={Nafis Sadeq and Xin Xu and Zhouhang Xie and Julian McAuley and Byungkyu Kang and Prarit Lamba and Xiang Gao},
      year={2025},
      eprint={2504.10647},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2504.10647}, 
}