Posts

HugNLP

HugNLP

AI
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HugNLP is a novel development and application library based on Hugging Face for improving the convenience and effectiveness of NLP researchers. The founder and main developer is Jianing Wang. The collaborators (programmers) are Nuo Chen and Qiushi Sun. Link
Incredibile pyTorch

Incredibile pyTorch

deep-learning, python
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This is a curated list of tutorials, projects, libraries, videos, papers, books and anything related to the incredible PyTorch. Feel free to make a pull request to contribute to this list. Link
LMOps

LMOps

Machine Learning, data-engineering
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LMOps is a research initiative on fundamental research and technology for building AI products w/ foundation models, especially on the general technology for enabling AI capabilities w/ LLMs and Generative AI models. Link
ML Systems Industrialization and MLOps

ML Systems Industrialization and MLOps

data-engineering, Machine Learning
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Industrialization of Data Consumption by ML systems during both experimentation (historical batch) and production (batch & stream) — grow above & beyond toy-ML-with-csv and single-threaded-pickle-flasked-deployment. [Link]{https://medium.com/@sunil_iitb/ml-systems-industrialization-and-mlops-b30106974454}
MosaicML StreamingDataset: Fast, Accurate Streaming of Training Data from Cloud Storage

MosaicML StreamingDataset: Fast, Accurate Streaming of Training Data from Cloud Storage

Machine Learning
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Loading your training data becomes an escalating challenge as datasets grow bigger in size and the number of nodes scales. We built StreamingDataset to make training on large datasets from cloud storage as fast, cheap, and scalable as possible. Specially designed for multi-node, distributed training, StreamingDataset maximizes correctness guarantees, performance, and ease of use. Link
Prompt Engineering Guide

Prompt Engineering Guide

AI
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Prompt engineering is a relatively new discipline for developing and optimizing prompts to efficiently use language models (LMs) for a wide variety of applications and research topics. Prompt engineering skills help to better understand the capabilities and limitations of large language models (LLMs). Researchers use prompt engineering to improve the capacity of LLMs on a wide range of common and complex tasks such as question answering and arithmetic reasoning. Developers use prompt engineering to design robust and effective prompting techniques that interface with LLMs and other tools.
System design interview

System design interview

data-engineering
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System design is a very broad topic. Even a software engineer with many years of working experience at a top IT company may not be an expert on system design. If you want to become an expert, you need to read many books, articles, and solve real large scale system design problems. Link