🤩 𝐀𝐰𝐞𝐬𝐨𝐦𝐞-𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧-𝐋𝐋𝐌

This repository contains a curated list of awesome open-source libraries for production large language models.

59 projects are selected with high standards.

• 📚LLM Data Preprocessing (6)
• 🤖LLM Training / Finetuning (12)
• 📊LLM Evaluation / Benchmark (6)
• 🚀LLM Serving / Inference (12)
• 🛠️LLM Application / RAG (12)
• 🧐LLM Testing / Monitoring (7)
• 🛡️LLM Guardrails / Security (4)

Github 👉 https://github.com/jihoo-kim/awesome-production-llm

What’s your take on this framework?

Any additional architectural considerations you’d add for enterprise-grade LLM applications?

P.S. Curious about the latest in LLM efficiency? Check out the recent papers on model distillation and quantization.

A 7-Step Technical Framework

Architecting Robust LLM-Powered Applications

Let’s dive deep into the architecture of Language Model (LLM) powered applications.

Here’s a comprehensive framework to guide your next cutting-edge project:

🌴🔬 𝗕𝗼𝗻𝘂𝘀: Join me for an advanced workshop on LLM application architecture.

We’ll cover topics like building Robust Real-Time AI Apps on Iceberg Data

1️⃣ Define Application Scope and User Interaction Model

• Identify core use cases and potential edge cases
• Design the user interaction flow (e.g., multi-turn dialogues, single-query responses)
• Consider scalability and performance requirements

2️⃣ Engineer Prompt Chain Architecture

• Implement prompt engineering techniques (e.g., few-shot learning, chain-of-thought)
• Develop a robust prompt template system with version control
• Optimize for token efficiency and response coherence

3️⃣ Implement Stateful Conversations with Advanced Memory Buffers

• Choose appropriate memory structures (e.g., sliding window, summary buffers)
• Implement efficient serialization and deserialization of conversation state
• Design memory management strategies for long-running sessions

4️⃣ Integrate Retrieval-Augmented Generation (RAG) and Tool Use

• Implement vector databases for semantic search capabilities
• Develop a flexible tool-use framework (consider the OpenAI function calling paradigm)
• Design fall-back mechanisms for API failures or out-of-domain queries

5️⃣ Establish Robust Data Processing Pipeline

• Implement ETL processes for diverse data sources
• Develop efficient indexing strategies for quick retrieval
• Design data validation and sanitization protocols

6️⃣ Rigorous Testing and Iterative Refinement

• Implement comprehensive unit and integration testing suites
• Develop metrics for response quality, latency, and coherence
• Utilize A/B testing for prompt and model optimization

7️⃣ Production Deployment and Monitoring

• Containerize your application for consistent deployment
• Implement robust logging and telemetry
• Design auto-scaling mechanisms to handle variable load