- I'm Lei Shang. Thanks for visiting my GitHub space!
- 🔭 I'm currently working on Generative AI frameworks, exploring use cases and the underlying technology.
- 📫 Feel free to reach out for a chat if you come across something interesting:
- Generative AI
- Data Science in Action
- Programming Languages
- Data Science Toolkit
- MLOps
- Cloud Computing
- Clean Code
- Research
Exploring the Trend, Technology, and Latest Advances
| Topic | Code/Link |
|---|---|
| LangSmith | Get Started with LangSmith |
| Llama 2 | Running Llama 2 on Your Local GPU |
| Llama 2 | How to Fine-Tune Llama 2 With Scale's LLM Engine |
| Notes | Generative AI Notes |
| LLM Architectures and Applications | Emerging Architectures for LLM Applications |
| Notes | Use case: Recommendation using embeddings and nearest neighbor search |
| Notes | Use case: Retrieval Augmented Generation; Question Answering Over Documents through RAG (Retrieval Augmented Generation) |
| Notes | Use case: Embedding as a text feature encoder for ML algorithms |
| Notes | Top 5 Vector Database Solutions for Your AI Project |
| Prompt Engineering | 🚧 |
| LangChain Core Concepts (Chains, Agents, Tools) | 🚧 |
| LangChain Core Concepts (Document loaders for unstructured file) | 🚧 |
| LangChain Core Concepts (Text Splitters) | 🚧 |
| LangChain Core Concepts (Vector stores) | 🚧 |
| LLaMA 2 - Every Resource you need | 🔗 |
| RAGs | Introducing RAGs: Your Personalized ChatGPT Experience Over Your Data |
Real-World Applications and Case Studies
| Topic | Code/Link |
|---|---|
| LLM empowered sentiment analysis | 🚧 |
| Scale document question answering using LLMs | 🔗 |
| Efficiently Applying LLMs to Transform Semi-Structured Data | News; Code and Paper |
| LLM empowered personalisation and recommendation systems | 🚧 |
| Question Answering Over Documents through RAG (Retrieval Augmented Generation) | 🚧 |
| Predicting the Financial Market with Large Language Models | 🔗 |
| Topic | Code/Link |
|---|---|
| Scala | Scala |
| Topic | Code/Link |
|---|---|
| Pandas | Pandas examples |
| Docker | 🚧 |
Productionising machine learning models
| Topic | Code/Link |
|---|---|
| FastAPI and Docker Compose | Quick Start with FastAPI and Docker |
| Vertex AI | 🚧 |
| AWS SageMaker | 🚧 Deploy a serverless ML inference endpoint of large language models using FastAPI, AWS Lambda, and AWS CDK |
| AWS Lambda | 🚧 (An example using FastAPI, Docker and AWS Lambda) |
| Kubeflow | 🚧 |
| MLflow and Kubernetes | ML deployment examples |
| Terraform | 🚧 |
| Topic | Code/Link |
|---|---|
| AWS | 🚧 |
| GCP | 🚧 |
| MS Azure | 🚧 |
| Topic | Code/Link |
|---|---|
| LeetCode | 🚧 |
| SOLID Principles in Python | 🚧 |
Papers & Books that I'm following
| Topic | Code/Link |
|---|---|
| Efficient Finetuning of Quantized LLMs | QLoRA |
| ChatGPT | How Is ChatGPT’s Behavior Changing over Time? |
| NLP | Attention Is All You Need |
| Time Series Forecasting | Deep GPVAR: Upgrading DeepAR For Multi-Dimensional Forecasting |
| LLM | Aligning language models to follow instructions |
| LLM | Making fine-tuning cheap & accessible |
| LLM | Llama-2 7B Chat |
| LLM | Data Mining Research Group at Tsinghua University |
| Bayesian Modeling and Computation | Bayesian Modeling and Computation in Python |
| Statistics and Data Analysis | Statistical Thinking for the 21st Century |
| Reinforcement Learning | Reinforcement Learning: An Introduction |
| AWS Labs | https://github.com/awslabs |
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