yangxw/Llama-3.2-1B-countdown-backtrack

Model Card for Model ID

Self-Backtracking: A novel self-backtracking method for improving language model reasoning, as described in Step Back to Leap Forward: Self-Backtracking for Boosting Reasoning of Language Models.

Model Details

• Developed by: Xiao-Wen Yang and Xuan-Yi Zhu and Wen-Da Wei and Ding-Chu Zhang and Jie-Jing Shao and Zhi Zhou and Lan-Zhe Guo and Yu-Feng Li
• Model type: Llama
• Language(s) (NLP): en
• License: mit
• Finetuned from model: Llama 3.2

Model Sources

• Repository: https://github.com/LAMDASZ-ML/Self-BackTracking
• Paper: Step Back to Leap Forward: Self-Backtracking for Boosting Reasoning of Language Models

Uses

The integration of slow-thinking mechanisms into large language models (LLMs) offers a promising way toward achieving Level 2 AGI Reasoners.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("yangxw/Llama-3.2-1B-countdown-backtrack")
model = AutoModelForCausalLM.from_pretrained("yangxw/Llama-3.2-1B-countdown-backtrack")

prompt = "What is 2 + 2?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))