Full-stack LLM Engineering Lab. Features: Autonomous Agents (ReAct/AutoGPT) | Fine-Tuning Llama/Mistral (SFT/DPO) | Large Model Deployment (DeepSeek 671B / 2.5-bit) | Advanced RAG (Hybrid Search) | Function Calling (Stream/Text-to-SQL/External APIs) | Frameworks (LangChain, Semantic Kernel, OpenAI) | Daily SOTA Paper Tracking. From theory to 0-to-1

LLMs-Lab

Full-stack LLM Engineering Lab. Features: Autonomous Agents (ReAct/AutoGPT) | Fine-Tuning Llama/Mistral (SFT/DPO) | Large Model Deployment (DeepSeek 671B / 2.5-bit) | Advanced RAG (Hybrid Search) | Function Calling (Stream/Text-to-SQL/External APIs) | Frameworks (LangChain, Semantic Kernel, OpenAI) | Daily SOTA Paper Tracking. From theory to 0-to-1.

9. 📂 DeepSeek

Focused on Inference Optimization and Low-Bit Quantization strategies for massive-scale MoE models (600B+ parameters).

• DeepSeek-R1 (671B) 2.51-bit Extreme Quantization Deployment:
  • Deployed the 2.51-bit quantized version (via Unsloth) of the 671B MoE model, achieving an ~80% reduction in memory footprint (from 720GB to ~212GB).
  • Analyzed official and community benchmarks for 1.58-bit vs 2.51-bit configurations, ultimately selecting the 2.51-bit build (Q2_K_XL) to ensure superior reasoning stability on the H20 GPU cluster.
  • 📄 View Hands-on Deployment Log & Benchmarks (PDF)

8. 📂 Fine-Tuning

This directory bridges the gap between theoretical architecture analysis and practical, memory-efficient fine-tuning of state-of-the-art open-source models. It covers the full lifecycle from pre-training understanding to post-training alignment.

Key Modules

• Transformer Source Code Analysis: A deep dive into the vanilla Transformer architecture, focusing on a line-by-line implementation analysis of Self-Attention mechanisms, Multi-Head Attention, and Layer Normalization to understand the foundational building blocks.

• Llama Series: QLoRA & Quantization: Implementation of QLoRA (Quantized Low-Rank Adapters) to fine-tune L