LLM-based agents for Software Engineering
"Large Language Model-Based Agents for Software Engineering: A Survey".

https://github.com/FudanSELab/Agent4SE-Paper-List.

https://t.iss.one/deep_learning_proj

Welcome to Ollama's Prompt Engineering Interactive Tutorial

🔗 Github

https://t.iss.one/deep_learning_proj

Fine Tuning LLMs with Hugging Face LLMs Code

https://t.iss.one/deep_learning_proj

Foundations of Large Language Models

📝 Table of Contents:
● Pre-training
● Generative Models
● Prompting
● Alignment

Tong Xiao and Jingbo Zhu
January 17, 2025

📃 Download from arXiv.

@Machine_learn

Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap

Large language models (LLMs) have not only revolutionized natural language processing but also extended their prowess to various domains, marking a significant stride towards artificial general intelligence. The interplay between LLMs and evolutionary algorithms (EAs), despite differing in objectives and methodologies, share a common pursuit of applicability in complex problems. Meanwhile, EA can provide an optimization framework for LLM's further enhancement under black-box settings, empowering LLM with flexible global search capacities. On the other hand, the abundant domain knowledge inherent in LLMs could enable EA to conduct more intelligent searches. Furthermore, the text processing and generative capabilities of LLMs would aid in deploying EAs across a wide range of tasks. Based on these complementary advantages, this paper provides a thorough review and a forward-looking roadmap, categorizing the reciprocal inspiration into two main avenues: LLM-enhanced EA and EA-enhanced #LLM. Some integrated synergy methods are further introduced to exemplify the complementarity between LLMs and EAs in diverse scenarios, including code generation, software engineering, neural architecture search, and various generation tasks. As the first comprehensive review focused on the EA research in the era of #LLMs, this paper provides a foundational stepping stone for understanding the collaborative potential of LLMs and EAs. The identified challenges and future directions offer guidance for researchers and practitioners to unlock the full potential of this innovative collaboration in propelling advancements in optimization and artificial intelligence.

Paper: https://arxiv.org/pdf/2401.10034v3.pdf

Code: https://github.com/wuxingyu-ai/llm4ec

https://t.iss.one/deep_learning_proj

How to run 🐋 DeepSeek locally on your Computer

@Machine_learn

🐫Tülu 3 (what a name) 405B - ​​another release!

An open source model (and no, it's not a Chinese model) that outperforms the DeepSeek-V3! on multiple benchmarks

Scalable to 405B - ​​with performance on par with GPT-4o and outperforming previous models in the same class.

▪ Blog: https://allenai.org/blog/tulu-3-405B
▪You can test it here: https://playground.allenai.org/?model=tulu3-405b
▪ Technical report: https://allenai.org/blog/tulu-3-technical
▪ Hugging Face : https://huggingface.co/collections/allenai/tulu-3-models-673b8e0dc3512e30e7dc54f5

https://t.iss.one/deep_learning_proj

⚡ LitGPT



pip install 'litgpt[all]'



from litgpt import LLM

llm = LLM.load("microsoft/phi-2")
text = llm.generate("Fix the spelling: Every fall, the familly goes to the mountains.")
print(text)
# Corrected Sentence: Every fall, the family goes to the mountains.

▪Github
▪Docs
▪Video

https://t.iss.one/deep_learning_proj