Open LLM Tech: Core Technologies
Days
TR212
This track focuses on the underlying core technologies and development workflows of Large Language Models (LLMs), rather than the generated output. We welcome technical submissions regarding model training, architectural optimization, and deployment practices. Topics include, but are not limited to:
- Model Architecture & Training Practices Explore model structural design, pre-training, and fine-tuning techniques such as LoRA or QLoRA, and methods to improve training efficiency.
- Quantization & Deployment Optimization Share insights on model quantization, optimization of inference engines (such as llama.cpp, vLLM), and achieving high-performance deployment across various hardware environments.
- Dataset Processing & Open Standards Discuss the cleaning and labeling processes for high-quality training data, and practices to ensure datasets and model weights comply with open-licensing standards.
Day 1
August 8, 2026 · TR212
TR212
9:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
Mechanistic interpretability and applications: The final fronteir of hacking
Martin Chang
Intermediate
Breaking FP64 Limits: AdaptiveGEMM Achieves Near-A100 Performance on RTX 4060 using INT8 Tensor Cores
Tsai,Ming-Han
Advanced
YAML is the New Dockerfile: Building AI Agent Systems with Docker cagent
Hrittik Roy, Parth Goswami
Intermediate
A Prompt Is Not All You Need: Building a LLM Synthetic Data Pipeline with Open-Source Tools
Nero Un 阮智軒
Intermediate
An Alternative Encoding Method: Reducing Semantic Retrieval from O(n²) to O(n)
galaxy4552
Intermediate