- Objective Exploration: Investigates the disparities between full fine-tuning (FT) and LoRA through a novel weight decomposition analysis.
- Innovative Method: Introduces Weight-Decomposed LowRank Adaptation (DoRA), which splits pre-trained weights into magnitude and direction for fine-tuning.
- Strategic Approach: Employs LoRA for directional updates, significantly reducing the number of trainable parameters.
- Enhanced Performance: By adopting DoRA, it improves learning capacity and training stability of LoRA, without extra inference costs.
- Proven Superiority: Demonstrates that DoRA outperforms LoRA in fine-tuning LLAMA, LLaVA, and VL-BART on tasks like commonsense reasoning, visual instruction tuning, and image/video-text understanding.
- https://arxiv.org/abs/2402.09353
Archives quotidiennes :
Bunkatopics
Bunkatopics is a package designed for Data Cleaning, Topic Modeling Visualization and Frame Analysis. Its primary goal is to assist developers in gaining insights from unstructured data, potentially facilitating data cleaning and optimizing LLMs through fine-tuning processes. Bunkatopics is constructed using well-known libraries like langchain, chroma, and transformers, enabling seamless integration into various environments.
https://github.com/charlesdedampierre/BunkaTopics?tab=readme-ov-file
LORAX
Multi-LoRA inference server that scales to 1000s of fine-tuned LLMs
LoRAX (LoRA eXchange) is a framework that allows users to serve thousands of fine-tuned models on a single GPU, dramatically reducing the cost of serving without compromising on throughput or latency.
LiPO: Listwise Preference Optimization through Learning-to-Rank
- Innovative Framework: LiPO revolutionizes language model alignment by approaching it as a listwise ranking challenge.
- Cutting-Edge Techniques: Utilizes advanced LTR algorithms for a more refined optimization process.
- Superior Performance: LiPO-X method surpasses traditional methods in aligning models with human preferences.
Enhanced Learning Efficiency: Offers a more effective learning paradigm from ranked response lists.
- Scalable Solution: Shows promise for scaling up to larger language model policies across various applications
PyOD, a versatile Python library for detecting anomalies in multivariate data.
Whether you’re tackling a small-scale project or large datasets, PyOD offers a range of algorithms to suit your needs.
- For time-series outlier detection, please use TODS.
- For graph outlier detection, please use PyGOD.
- Performance Comparison & Datasets: We have a 45-page, the most comprehensive anomaly detection benchmark paper. The fully open-sourced ADBench compares 30 anomaly detection algorithms on 57 benchmark datasets.
- Learn more about anomaly detection @ Anomaly Detection Resources
- PyOD on Distributed Systems: you could also run PyOD on databricks.