If you want to learn about LLMs in just 3 hours, two lectures given by Yann Dubois are just what you need:
Overview of LLM training and post-training:
Scalable LLM evaluation:
If you want to learn about LLMs in just 3 hours, two lectures given by Yann Dubois are just what you need:
Overview of LLM training and post-training:
Scalable LLM evaluation:
FROG: Frobenius-guided Relevance Optimization with Guided noise for Efficient Fine-tuning of Large Language and Vision Models
This paper introduces FROG (Frobenius-guided Relevance Optimization with Guided noise), an innovative approach for efficient fine-tuning of large language models (LLMs) and vision models (VLMs). FROG combines SVD-based weight relevance scoring, Frobenius norm-based matrix importance calculation, and dynamic noise injection to significantly reduce the number of trainable parameters while maintaining model performance. This method enables faster and more resource-efficient fine-tuning of large pre-trained models for specific tasks.
As LLMs and VLMs continue to grow in size and complexity, fine-tuning these models for specific tasks becomes increasingly challenging due to computational and memory constraints. FROG addresses this challenge by intelligently selecting a subset of weights to fine-tune based on their relevance and the importance of their respective matrices. This approach not only reduces the computational requirements but also helps maintain the pre-trained knowledge while adapting to new tasks.
Fine-tuning large pre-trained models has become a standard practice in transfer learning for natural language processing and computer vision tasks. However, traditional fine-tuning methods often require updating all model parameters, which can be computationally expensive and may lead to catastrophic forgetting. Recent research has focused on parameter-efficient fine-tuning methods, such as adapter layers and sparse fine-tuning, to address these issues.
The FROG method builds upon the intuition that not all weights in a neural network contribute equally to the model’s performance. By analyzing the structure of weight matrices through SVD, we can identify the most influential weights and focus the fine-tuning process on these parameters.
The weight relevance scoring in FROG is based on the SVD decomposition of weight matrices and the relationship between weights and singular values. This approach considers the weight’s impact on the overall transformation performed by the weight matrix.
For each weight matrix W in the model:
This scoring method computes the relevance of each weight by summing its contributions across all singular value components. Weights that contribute significantly across multiple components will have higher relevance scores.
The absolute value is used to focus on the magnitude of the contribution, regardless of its direction. This approach ensures that we capture the full importance of each weight across the entire transformation represented by the weight matrix.
Initialize trainable weights based on their relevance scores:
w_ij_init = α * S_ij * N(0, 1)
where α is a scaling factor and N(0, 1) is a standard normal distribution
This initialization scheme ensures that the initial values of the trainable weights are proportional to their relevance scores, allowing the most important weights to have a larger initial impact on the fine-tuning process.
During training:
The dynamic noise injection serves two purposes:
After applying the FROG method to individual matrices:
Given a global percentage G% of trainable weights:
FROG presents a novel approach to efficient fine-tuning of large language and vision models by combining SVD-based weight relevance scoring, Frobenius norm-based matrix importance calculation, and dynamic noise injection. This method significantly reduces the number of trainable parameters while maintaining model performance, enabling faster and more resource-efficient adaptation of large pre-trained models to specific tasks. The incorporation of Frobenius norm-based matrix importance allows for a more nuanced distribution of trainable weights across the model, potentially improving the efficiency of weight selection for fine-tuning. As the field of AI continues to advance with increasingly large and complex models, techniques like FROG will play a crucial role in making these models more accessible and adaptable to a wide range of applications.
While our initial analysis focused on the global percentage of selected weights across the entire model, it’s crucial to understand the importance of individual layers. The FROG method inherently calculates a matrix score for each layer, which provides a direct measure of layer importance.
To gain deeper insights into the model’s structure and the relative importance of each layer, we propose a simple yet effective visualization:
Bar Graph of Layer Importance:
This visualization offers several benefits:
By examining this layer importance graph, we can:
This layer-specific analysis opens up several avenues for future research:
By incorporating this layer importance analysis, we enhance our understanding of the model’s internal structure and can potentially develop more efficient and effective fine-tuning approaches.
While our current method selects weights independently within each layer, we propose an alternative approach that could offer new insights into the global importance of weights across the entire model.
Instead of selecting a percentage of weights from each layer individually, we suggest ranking all weights across the model and selecting the top X% globally. This approach has several potential implications:
In this global selection framework, we can redefine layer importance as follows:
This redefinition could lead to a more nuanced understanding of how different parts of the model contribute to its overall function.
This global approach to weight selection could offer several advantages:
By exploring this global weight selection approach, we may uncover new perspectives on model structure and function, potentially leading to more efficient and effective fine-tuning strategies.
Author : Loic Baconnier
A Novel Approach for Efficient Fine-Tuning of Large Language Models
We present Singular Value-Rotation Adaptation with Full Rank (SVRA-FR), a novel method for efficient fine-tuning of large language models. SVRA-FR leverages the full singular value decomposition (SVD) of weight matrices, allowing for comprehensive adjustments through singular value modification and singular vector rotation. This approach offers a parameter-efficient, interpretable, and potentially more effective alternative to existing fine-tuning methods, particularly Low-Rank Adaptation (LoRA).
Large language models have demonstrated remarkable performance across various natural language processing tasks. However, fine-tuning these models for specific tasks remains computationally expensive and often requires significant amounts of data. Recent work on parameter-efficient fine-tuning methods, such as LoRA, has shown promise in reducing these costs. Our work builds upon these approaches by introducing a method that directly manipulates the full SVD components of weight matrices.
SVRA-FR consists of the following key components:
We begin by performing SVD on the original weight matrix W:
W = UΣV^T
where U and V are orthogonal matrices containing left and right singular vectors, respectively, and Σ is a diagonal matrix of singular values. This decomposition allows us to represent the weight matrix in terms of its principal components, with singular values indicating the importance of each component.
SVRA-FR introduces three sets of trainable parameters:
a) Δσ: A vector for adjusting all singular values
b) θ_U: A vector for rotating all left singular vectors
c) θ_V: A vector for rotating all right singular vectors
These parameters allow for fine-grained control over the matrix’s structure and information content.
We modify all singular values:
σ’_i = σ_i + Δσ_i
This adjustment allows us to amplify or attenuate the importance of different components in the weight matrix. By modifying singular values, we can control the « strength » of different features or directions in the weight space.
We apply rotation to all left and right singular vectors:
u’_i = R(θ_U_i)u_i
v’_i = R(θ_V_i)v_i
where R(θ) is a 2D rotation matrix:
R(θ) = [cos(θ) -sin(θ); sin(θ) cos(θ)]
Rotation of singular vectors allows us to adjust the directions of the principal components in the weight space. This can be particularly useful for aligning the model’s features with task-specific requirements without drastically changing the overall structure of the weight matrix.
We reconstruct the adaptation matrix:
W_adapt = U’Σ’V’^T
where U’ and V’ contain the rotated singular vectors and Σ’ is the diagonal matrix of adjusted singular values. This reconstruction combines the effects of singular value adjustments and vector rotations into a single adaptation matrix.
The final weight update is applied additively:
W_new = W + αW_adapt
where α is a scaling factor. This additive update allows us to preserve the original pre-trained weights while incorporating task-specific adaptations.
SVRA-FR differs from LoRA in several key aspects:
For a weight matrix of size m x n, SVRA-FR introduces min(m, n) + m + n trainable parameters, compared to LoRA’s 2r(m+n), where r is the LoRA rank. For large matrices and typical LoRA ranks, SVRA-FR is often more parameter-efficient. This efficiency stems from directly modifying the SVD components rather than introducing separate low-rank matrices.
Unlike LoRA, which uses low-rank matrices, SVRA-FR works with the full SVD, potentially allowing for more comprehensive adaptations. This full-rank approach enables adjustments across the entire weight space, which may be beneficial for tasks requiring fine-grained modifications.
SVRA-FR directly modifies the singular values and vectors of the original matrix, potentially preserving more of the pre-trained structure. This direct manipulation allows for more interpretable changes and may lead to better preservation of the model’s original capabilities.
SVRA-FR offers a novel approach to fine-tuning large language models by directly manipulating their SVD structure. This method combines the efficiency of parameter-efficient fine-tuning techniques with the comprehensiveness of full-rank adaptations. By allowing for targeted adjustments to singular values and rotations of singular vectors, SVRA-FR provides a flexible framework for adapting pre-trained models to specific tasks.
The full-rank nature of SVRA-FR is a key differentiator from methods like LoRA. While this could potentially lead to more comprehensive adaptations, it also raises questions about the trade-off between flexibility and the risk of overfitting. Empirical studies will be crucial to understand these trade-offs across various tasks and model sizes.
Future research directions include:
SVRA-FR represents a promising new direction in efficient fine-tuning of large language models. By leveraging the full SVD structure of weight matrices, it offers a parameter-efficient, interpretable, and flexible approach to model adaptation. While further empirical validation is needed, SVRA-FR has the potential to significantly improve the efficiency and effectiveness of fine-tuning large language models for specific tasks, particularly in scenarios where comprehensive adaptations are beneficial. The method’s ability to directly manipulate the core structure of weight matrices opens up new possibilities for understanding and controlling the adaptation process in deep learning models.
Sources: Loic Baconnier
In the realm of document retrieval and search, combining cutting-edge technologies can lead to powerful and efficient systems. This article explores the integration of Qdrant, ColQwen, and MOLMO to create a sophisticated document retrieval pipeline that prioritizes privacy and on-premise deployment.
Qdrant: Multi-Vector Capabilities
Qdrant is an open-source vector similarity search engine designed for high-performance at scale. Its multi-vector feature allows storing multiple vectors per object within a single collection, offering several advantages:
MOLMO: Multimodal Open Language Model
MOLMO (Multimodal Open Language Model) is a family of open vision-language models developed by the Allen Institute for AI. Key features include:
ColQwen: Efficient Visual Document Retriever
ColQwen is a visual retriever model based on Qwen2-VL-2B-Instruct, implementing the ColBERT strategy. Key aspects include:
Integrating Qdrant, MOLMO, and ColQwen for Secure, On-Premise Document Retrieval
Document Processing:
Indexing with Qdrant:
Query Processing:
Retrieval and Ranking:
Result Enhancement:
Privacy and Security Advantages
Conclusion
The integration of Qdrant’s multi-vector capabilities, ColQwen’s efficient document representation, and MOLMO’s multimodal understanding creates a powerful, secure, and privacy-focused document retrieval system. This approach allows organizations to leverage advanced AI technologies for document analysis while maintaining complete control over their sensitive information, making it particularly valuable for industries dealing with confidential data, such as legal firms, healthcare providers, financial institutions, or government agencies.
MOLMO:
MOLMO on Hugging Face
Qdrant:
Qdrant’s documentation
ColQwen:
ColQwen2 on Hugging Face
Machine learning is a rapidly evolving field with numerous algorithms designed to tackle various data science challenges. This article provides an overview of 101 machine learning algorithms, categorized by their primary functions.
Classification algorithms predict outcome classes for given datasets. Here are some key examples:
Regression algorithms examine relationships between variables. Some popular regression algorithms include:
Neural networks are artificial models inspired by the human brain. Some common types include:
Anomaly detection algorithms find rare occurrences or suspicious events in data:
These algorithms reduce the number of random variables in a dataset:
Ensemble methods combine multiple algorithms to improve overall performance:
Clustering assigns labels to unlabeled data based on patterns:
These algorithms uncover associations between items:
Regularization prevents overfitting:
This comprehensive list of 101 machine learning algorithms covers a wide range of techniques used in data science. For more detailed information on each algorithm and when to use them, refer to the cheat sheets provided by Scikit-Learn.
Sources
101 Machine Learning Algorithms: A Comprehensive Guide
Prompt engineering has become a crucial skill in the era of advanced language models. Whether you’re a developer, researcher, or enthusiast working with AI, understanding how to effectively communicate with these models can significantly enhance your results. Here are 20 key tips to improve your prompt engineering skills:
By incorporating these tips into your prompt engineering practice, you can significantly improve your interactions with AI language models. Remember that the effectiveness of these strategies may vary depending on the specific task and model you’re working with. Continuous experimentation and refinement of your approach will lead to the best results in prompt engineering.
Sources
The biggest challenge in building a pre-trained model for time series is finding high-quality and diverse data. This difficulty is at the core of developing effective forecasting models.
Two primary approaches are used to build a fundamental forecasting model:
The second approach has proven more effective, as evidenced by models such as MOIRAI, TimesFM, and TTM. However, these models follow scaling laws, and their performance heavily depends on the availability of extensive time series data, which brings us back to the initial challenge.
Faced with these limitations, an innovative approach was explored: using a different modality, namely images. Although counterintuitive, this method has produced groundbreaking results, opening new perspectives in the field of time series forecasting.
VisionTS represents a novel approach that leverages the power of image-based models for time series forecasting. This method transforms time series data into images, allowing the use of advanced computer vision techniques to predict future values.
Using images for time series forecasting offers several advantages:
The success of VisionTS suggests a promising direction for future research in time series forecasting. It demonstrates the potential of cross-modal learning and opens up new possibilities for improving prediction accuracy and generalization in various domains.
paper:
https://arxiv.org/pdf/2408.17253
Code:
BILLION-SCALE TIME SERIES FOUNDATION MODELS From Princeton WITH MIXTURE OF EXPERTS
TIME-MOE is a scalable and unified architecture designed for pre-training large, capable forecasting foundation models while reducing inference costs. It addresses the limitations of current pre-trained time series models, which are often limited in scale and operate at high costs.
Positioned as a state-of-the-art solution for real-world time series forecasting challenges, offering superior capability, efficiency, and flexibility.
https://arxiv.org/pdf/2409.16040
Code:
Safe, interpretable, trustworthy AI, through interactive intelligent visualization, with applications in adversarial machine learning (protecting AI from harm and doing harm), scalable discoveries of deep learning models, and inclusive AI for everyone.
A Must.
Transformer Explainer
Learn How Transformer Models Work with Interactive Visualization
https://poloclub.github.io/transformer-explainer/
Diffusion Explainer
Learn how Stable Diffusion transforms your text prompt into image.