efficientscaling/Z1

[EMNLP'25 Industry] Repo for "Z1: Efficient Test-time Scaling with Code"

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Experimental

This project improves how Large Language Models (LLMs) reason, especially when faced with complex problems that require a series of logical steps. It takes a problem statement (or prompt) and enhances the LLM's ability to 'think' through it more effectively by generating intermediate steps or code. The output is a more accurate and robust final answer. This is primarily useful for AI researchers and practitioners working with advanced LLM applications.

No commits in the last 6 months.

Use this if you are working with Large Language Models and need to improve their accuracy and reasoning capabilities, especially for tasks requiring multi-step thought processes or code generation.

Not ideal if you are a general user looking for an off-the-shelf application or if your primary need is for simple, direct text generation without complex reasoning.

Large Language Models AI Reasoning Code Generation Natural Language Processing Model Fine-tuning
No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 8 / 25
Maturity 8 / 25
Community 4 / 25

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68

Forks

2

Language

Python

License

Last pushed

Apr 11, 2025

Commits (30d)

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