Discover the latest LLM implementations in production. 🚀
Learn how big tech companies and startups implement and leverage LLMs in 2024:
- How LLMs are deployed and integrated into large-scale applications 🔎
- What architectures and techniques worked when implementing LLM in the SWD cycle ✅ (Data Quality, Data Engineering, Serving, Monitoring 📈 etc)
- What real-world results were achieved (so you can better assess ROI ⏰💰)
- Why it works and what is the science behind it with research, literature, and references 📂
Feel free to contribute!
- Training and Fine-tuning Techniques
- Data Quality for LLMs
- Data Engineering with LLM
- LLM Deployment
- Evaluation and Metrics for LLMs
- Prompt Engineering
- Vector Stores
- Tools and Frameworks
- Retrieval Augmented Generation (RAG)
- Graph and LLMs
- Multimodal with LLMs
- Scaling and Optimization
- Ethical Considerations and Limitations
- LLM Seminal Papers
- Courses and Tutorials
- GitHub Repositories
- LLM Tools for Developers
- Team Structure and Strategy
- Newsletters to follow
- Imbue: Training a 70B Model from Scratch
- Google: PaLM 2 Technical Report
- Anthropic: Training language models to follow instructions with human feedback
- EleutherAI: The Pile: An 800GB Dataset of Diverse Text for Language Modeling
- Hugging Face: How we trained BLOOM, the world's largest open multilingual language model
- DeepMind: Training Compute-Optimal Large Language Models
- NVIDIA Megatron-Turing NLG
- Nvidia: Scale and Curate High-Quality Datasets for LLMs
- Data Quality Error Detection with LLMs
- IBM: How to ensure Data Quality and Reliability
- NVIDIA: Curating custom datasets for LLM training
- Harnessing the power of LLMs in Data Engineering
- Data Engineer 2.0
- Data Collection Magic
- Data Engineers, here is how LLMs can make your life easier
- Efficient Large Language Model serving with BentoML
- Databricks: Model Serving
- TensorRT-LLM: Toolkit to optimize inference of LLMs
- Datyabricks: Deploying Large Language Models in Production
- Patterns for Building LLM-based Systems & Products
- LLM Engineering Guide
- OpenAI: Evaluating Large Language Models Trained on Code
- EleutherAI: HELM - Holistic Evaluation of Language Models
- Google: Beyond the Imitation Game - Measuring and extrapolating the capabilities of language models
- DeepMind: Measuring Massive Multitask Language Understanding
- Best Practices for LLM Evaluation
- LLM Evaluation Framework
- Google Cloud: Best practices for prompt engineering with LLMs
- Prompt Engineering Guide
- OpenAI Cookbook: Techniques to improve reliability
- Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
- ReAct: Synergizing Reasoning and Acting in Language Models
- Building a Vector Search Engine with Faiss
- Introducing ScaNN: Efficient Vector Similarity Search
- Milvus: An Open Source Vector Database for Scalable Similarity Search
- Vector Similarity Search: From Basics to Production
- Weaviate: The Open Source Vector Database
- Qdrant: Vector Database for the Next Generation of AI Applications
- Hugging Face Transformers
- LangChain: Building applications with LLMs through composability
- OpenAI API
- Anthropic Claude API
- Llama.cpp: Inference of LLaMA model in pure C/C++
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
- Improving language models by retrieving from trillions of tokens
- Retrieval-Augmented Generation: A Survey
- REALM: Retrieval-Augmented Language Model Pre-Training
- Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering
- Atlas: Few-shot Learning with Retrieval Augmented Language Models
- In-Context Retrieval-Augmented Language Models
- Combining Knowledge Graphs and Large Language Models
- Graph Neural Networks and Language Models: A Powerful Combination
- Knowledge Graphs and Language Models: Bridging the Gap
- Enhancing Language Models with Knowledge Graph Embeddings
- Graph-augmented Learning for Language Understanding
- Integrating Knowledge Graphs with Large Language Models
- Graph-based Neural Language Models
- DALL·E 2: Extending Language Models to Images
- PaLM-E: An Embodied Multimodal Language Model
- Flamingo: a Visual Language Model for Few-Shot Learning
- CLIP: Connecting Text and Images
- Multimodal Few-Shot Learning with Frozen Language Models
- VisualBERT: A Simple and Performant Baseline for Vision and Language
- DeepSpeed: Deep learning optimization library
- Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
- ZeRO: Memory Optimizations Toward Training Trillion Parameter Models
- FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
- On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?
- Challenges and Applications of Large Language Models
- AI Ethics Guidelines Global Inventory
- Ethical and social risks of harm from Language Models
- Attention Is All You Need (Transformer Paper)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- GPT-3: Language Models are Few-Shot Learners
- LLaMA: Open and Efficient Foundation Language Models
- A Survey of Large Language Models
- Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond
- Awesome-LLM: A curated list of Large Language Model resources
- WebGPT: Browser-assisted question-answering with human feedback
- InstructGPT: Training language models to follow instructions with human feedback
- DeepLearning.AI: ChatGPT Prompt Engineering for Developers
- Coursera: Natural Language Processing Specialization
- Fast.ai: Practical Deep Learning for Coders
- Stanford CS224N: Natural Language Processing with Deep Learning
- Hugging Face: NLP Course
- Creme de la Creme of Free AI courses
- Awesome Machine Learning
- Applied ML
- Awesome Scalability
- Made with ML
- The Algorithms
- TensorFlow Models
- Transformers Examples
- Sentence Transformers
- OpenAI Playground
- Hugging Face Spaces
- Gradio: Build Machine Learning Web Apps
- Streamlit: The fastest way to build data apps
- LangChain: Building applications with LLMs through composability