Building LLM Models

Notes from article 

• Open-source tools like LangChain and LlamaIndex are current tools for developers to build apps that rely on external services and sources

The tools part is significant

• Tools - LlamaIndex, LangChain
• Vector databases - Pinecode, Milvus 
• Milvus - A cloud-native vector database, storage for next generation AI applications (by milvus-io)

Experiments to do

• Run Milvus using Python
• llama-llm
• Marketing Copy Generation [Public Documentation] - llama-llm
• Zilliz Cloud managed vector database.
• Langchain - Question Answering over Docs
• Langchain Alternatives

• gpt_index
• llama_index
• llama
• visual-chatgpt

Recent Models -  BloombergGPT, an LLM for finance.

Key Training Process

Reinforcement Learning with Human Feedback

2 - Training Process - Ref Link

The LLM Three-Pronged Strategy: Tune, Prompt, Reward

Cerebras-GPT: A Family of Open, Compute-efficient, Large Language Models

Knowledge Graph - provide an interface for querying the index and obtaining knowledge-augmented output

Ref - Link1, Link2

RLHF helps to build state-of-the-art models like ChatGPT.

Did you know that training RLHF LLMs involve 4 key steps?

Here’s an illustrated guide to the process:

1 of 5

— Surge AI (@HelloSurgeAI) March 27, 2023

xTuring is an open-source AI personalization software.

Koala: A Dialogue Model for Academic Research

Eight Things to Know about Large Language Models

Ref - Link

Keep Exploring!!!