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WizardCoder: Empowering Code Large Language Models with Evol-Instruct

📝 Summary:
WizardCoder is a Code LLM fine-tuned using Evol-Instruct for complex instructions. It significantly outperforms open-source and major closed LLMs on code generation benchmarks.

🔹 Publication Date: Published on Jun 14, 2023

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2306.08568
• PDF: https://arxiv.org/pdf/2306.08568
• Github: https://github.com/nlpxucan/WizardLM

🔹 Models citing this paper:
https://huggingface.co/WizardLMTeam/WizardCoder-Python-34B-V1.0
https://huggingface.co/WizardLMTeam/WizardCoder-15B-V1.0
https://huggingface.co/alpindale/WizardLM-2-8x22B

Datasets citing this paper:
https://huggingface.co/datasets/WizardLMTeam/WizardLM_evol_instruct_V2_196k
https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1
https://huggingface.co/datasets/WizardLMTeam/WizardLM_evol_instruct_70k

Spaces citing this paper:
https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard
https://huggingface.co/spaces/Intel/low_bit_open_llm_leaderboard
https://huggingface.co/spaces/FallnAI/Quantize-HF-Models

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For more data science resources:
https://xn--r1a.website/DataScienceT

#CodeLLM #LLM #AIE #CodeGeneration #EvolInstruct
IQuest-Coder-V1 Technical Report

📝 Summary:
The IQuest-Coder-V1 series presents new code LLMs using a multi-stage training paradigm to capture dynamic software logic. This approach achieves state-of-the-art performance in agentic software engineering and competitive programming tasks. The Loop variant also optimizes deployment efficiency.

🔹 Publication Date: Published on Mar 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.16733
• PDF: https://arxiv.org/pdf/2603.16733
• Project Page: https://iquestlab.github.io/release-1.0-2603/index.html
• Github: https://github.com/IQuestLab/IQuest-Coder-V1

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For more data science resources:
https://xn--r1a.website/DataScienceT

#CodeLLM #SoftwareEngineering #LargeLanguageModels #AIResearch #MachineLearning