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Continuum Knowledge
  • Continuum
  • Data
    • Datasets
      • Pre Training Data
      • Types of Fine Tuning
      • Self Instruct Paper
      • Self-Alignment with Instruction Backtranslation
      • Systematic Evaluation of Instruction-Tuned Large Language Models on Open Datasets
      • Instruction Tuning
      • Instruction Fine Tuning - Alpagasus
      • Less is More For Alignment
      • Enhanced Supervised Fine Tuning
      • Visualising Data using t-SNE
      • UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
      • Training and Evaluation Datasets
      • What is perplexity?
  • MODELS
    • Foundation Models
      • The leaderboard
      • Foundation Models
      • LLama 2 - Analysis
      • Analysis of Llama 3
      • Llama 3.1 series
      • Google Gemini 1.5
      • Platypus: Quick, Cheap, and Powerful Refinement of LLMs
      • Mixtral of Experts
      • Mixture-of-Agents (MoA)
      • Phi 1.5
        • Refining the Art of AI Training: A Deep Dive into Phi 1.5's Innovative Approach
      • Phi 2.0
      • Phi-3 Technical Report
  • Training
    • The Fine Tuning Process
      • Why fine tune?
        • Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?
        • Explanations in Fine Tuning
      • Tokenization
        • Tokenization Is More Than Compression
        • Tokenization - SentencePiece
        • Tokenization explore
        • Tokenizer Choice For LLM Training: Negligible or Crucial?
        • Getting the most out of your tokenizer for pre-training and domain adaptation
        • TokenMonster
      • Parameter Efficient Fine Tuning
        • P-Tuning
          • The Power of Scale for Parameter-Efficient Prompt Tuning
        • Prefix-Tuning: Optimizing Continuous Prompts for Generation
        • Harnessing the Power of PEFT: A Smarter Approach to Fine-tuning Pre-trained Models
        • What is Low-Rank Adaptation (LoRA) - explained by the inventor
        • Low Rank Adaptation (Lora)
        • Practical Tips for Fine-tuning LMs Using LoRA (Low-Rank Adaptation)
        • QLORA: Efficient Finetuning of Quantized LLMs
        • Bits and Bytes
        • The Magic behind Qlora
        • Practical Guide to LoRA: Tips and Tricks for Effective Model Adaptation
        • The quantization constant
        • QLORA: Efficient Finetuning of Quantized Language Models
        • QLORA and Fine-Tuning of Quantized Language Models (LMs)
        • ReLoRA: High-Rank Training Through Low-Rank Updates
        • SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models
        • GaLora: Memory-Efficient LLM Training by Gradient Low-Rank Projection
      • Hyperparameters
        • Batch Size
        • Padding Tokens
        • Mixed precision training
        • FP8 Formats for Deep Learning
        • Floating Point Numbers
        • Batch Size and Model loss
        • Batch Normalisation
        • Rethinking Learning Rate Tuning in the Era of Language Models
        • Sample Packing
        • Gradient accumulation
        • A process for choosing the learning rate
        • Learning Rate Scheduler
        • Checkpoints
        • A Survey on Efficient Training of Transformers
        • Sequence Length Warmup
        • Understanding Training vs. Evaluation Data Splits
        • Cross-entropy loss
        • Weight Decay
        • Optimiser
        • Caching
      • Training Processes
        • Extending the context window
        • PyTorch Fully Sharded Data Parallel (FSDP)
        • Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation
        • YaRN: Efficient Context Window Extension of Large Language Models
        • Sliding Window Attention
        • LongRoPE
        • Reinforcement Learning
        • An introduction to reinforcement learning
        • Reinforcement Learning from Human Feedback (RLHF)
        • Direct Preference Optimization: Your Language Model is Secretly a Reward Model
  • INFERENCE
    • Why is inference important?
      • Grouped Query Attention
      • Key Value Cache
      • Flash Attention
      • Flash Attention 2
      • StreamingLLM
      • Paged Attention and vLLM
      • TensorRT-LLM
      • Torchscript
      • NVIDIA L40S GPU
      • Triton Inference Server - Introduction
      • Triton Inference Server
      • FiDO: Fusion-in-Decoder optimised for stronger performance and faster inference
      • Is PUE a useful measure of data centre performance?
      • SLORA
  • KNOWLEDGE
    • Vector Databases
      • A Comprehensive Survey on Vector Databases
      • Vector database management systems: Fundamental concepts, use-cases, and current challenges
      • Using the Output Embedding to Improve Language Models
      • Decoding Sentence-BERT
      • ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
      • SimCSE: Simple Contrastive Learning of Sentence Embeddings
      • Questions Are All You Need to Train a Dense Passage Retriever
      • Improving Text Embeddings with Large Language Models
      • Massive Text Embedding Benchmark
      • RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking
      • LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
      • Embedding and Fine-Tuning in Neural Language Models
      • Embedding Model Construction
      • Demystifying Embedding Spaces using Large Language Models
      • Fine-Tuning Llama for Multi-Stage Text Retrieval
      • Large Language Model Based Text Augmentation Enhanced Personality Detection Model
      • One Embedder, Any Task: Instruction-Finetuned Text Embeddings
      • Vector Databases are not the only solution
      • Knowledge Graphs
        • Harnessing Knowledge Graphs to Elevate AI: A Technical Exploration
        • Unifying Large Language Models and Knowledge Graphs: A Roadmap
      • Approximate Nearest Neighbor (ANN)
      • High Dimensional Data
      • Principal Component Analysis (PCA)
      • Vector Similarity Search - HNSW
      • FAISS (Facebook AI Similarity Search)
      • Unsupervised Dense Retrievers
    • Retrieval Augmented Generation
      • Retrieval-Augmented Generation for Large Language Models: A Survey
      • Fine-Tuning or Retrieval?
      • Revolutionising Information Retrieval: The Power of RAG in Language Models
      • A Survey on Retrieval-Augmented Text Generation
      • REALM: Retrieval-Augmented Language Model Pre-Training
      • Retrieve Anything To Augment Large Language Models
      • Generate Rather Than Retrieve: Large Language Models Are Strong Context Generators
      • Active Retrieval Augmented Generation
      • DSPy: LM Assertions: Enhancing Language Model Pipelines with Computational Constraints
      • DSPy: Compiling Declarative Language Model Calls
      • DSPy: In-Context Learning for Extreme Multi-Label Classification
      • Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs
      • HYDE: Revolutionising Search with Hypothetical Document Embeddings
      • Enhancing Recommender Systems with Large Language Model Reasoning Graphs
      • Retrieval Augmented Generation (RAG) versus fine tuning
      • RAFT: Adapting Language Model to Domain Specific RAG
      • Summarisation Methods and RAG
      • Lessons Learned on LLM RAG Solutions
      • Stanford: Retrieval Augmented Language Models
      • Overview of RAG Approaches with Vector Databases
      • Mastering Chunking in Retrieval-Augmented Generation (RAG) Systems
    • Semantic Routing
    • Resource Description Framework (RDF)
  • AGENTS
    • What is agency?
      • Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves
      • Types of Agents
      • The risk of AI agency
      • Understanding Personality in Large Language Models: A New Frontier in AI Psychology
      • AI Agents - Reasoning, Planning, and Tool Calling
      • Personality and Brand
      • Agent Interaction via APIs
      • Bridging Minds and Machines: The Legacy of Newell, Shaw, and Simon
      • A Survey on Language Model based Autonomous Agents
      • Large Language Models as Agents
      • AI Reasoning: A Deep Dive into Chain-of-Thought Prompting
      • Enhancing AI Reasoning with Self-Taught Reasoner (STaR)
      • Exploring the Frontier of AI: The "Tree of Thoughts" Framework
      • Toolformer: Revolutionising Language Models with API Integration - An Analysis
      • TaskMatrix.AI: Bridging Foundational AI Models with Specialised Systems for Enhanced Task Completion
      • Unleashing the Power of LLMs in API Integration: The Rise of Gorilla
      • Andrew Ng's presentation on AI agents
      • Making AI accessible with Andrej Karpathy and Stephanie Zhan
  • Regulation and Ethics
    • Regulation and Ethics
      • Privacy
      • Detecting AI Generated content
      • Navigating the IP Maze in AI: The Convergence of Blockchain, Web 3.0, and LLMs
      • Adverse Reactions to generative AI
      • Navigating the Ethical Minefield: The Challenge of Security in Large Language Models
      • Navigating the Uncharted Waters: The Risks of Autonomous AI in Military Decision-Making
  • DISRUPTION
    • Data Architecture
      • What is a data pipeline?
      • What is Reverse ETL?
      • Unstructured Data and Generatve AI
      • Resource Description Framework (RDF)
      • Integrating generative AI with the Semantic Web
    • Search
      • BM25 - Search Engine Ranking Function
      • BERT as a reranking engine
      • BERT and Google
      • Generative Engine Optimisation (GEO)
      • Billion-scale similarity search with GPUs
      • FOLLOWIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions
      • Neural Collaborative Filtering
      • Federated Neural Collaborative Filtering
      • Latent Space versus Embedding Space
      • Improving Text Embeddings with Large Language Models
    • Recommendation Engines
      • On Interpretation and Measurement of Soft Attributes for Recommendation
      • A Survey on Large Language Models for Recommendation
      • Model driven recommendation systems
      • Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations
      • Foundation Models for Recommender Systems
      • Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review
      • AI driven recommendations - harming autonomy?
    • Logging
      • A Taxonomy of Anomalies in Log Data
      • Deeplog
      • LogBERT: Log Anomaly Detection via BERT
      • Experience Report: Deep Learning-based System Log Analysis for Anomaly Detection
      • Log-based Anomaly Detection with Deep Learning: How Far Are We?
      • Deep Learning for Anomaly Detection in Log Data: A Survey
      • LogGPT
      • Adaptive Semantic Gate Networks (ASGNet) for log-based anomaly diagnosis
  • Infrastructure
    • The modern data centre
      • Enhancing Data Centre Efficiency: Strategies to Improve PUE
      • TCO of NVIDIA GPUs and falling barriers to entry
      • Maximising GPU Utilisation with Kubernetes and NVIDIA GPU Operator
      • Data Centres
      • Liquid Cooling
    • Servers and Chips
      • The NVIDIA H100 GPU
      • NVIDIA H100 NVL
      • Lambda Hyperplane 8-H100
      • NVIDIA DGX Servers
      • NVIDIA DGX-2
      • NVIDIA DGX H-100 System
      • NVLink Switch
      • Tensor Cores
      • NVIDIA Grace Hopper Superchip
      • NVIDIA Grace CPU Superchip
      • NVIDIA GB200 NVL72
      • Hopper versus Blackwell
      • HGX: High-Performance GPU Platforms
      • ARM Chips
      • ARM versus x86
      • RISC versus CISC
      • Introduction to RISC-V
    • Networking and Connectivity
      • Infiniband versus Ethernet
      • NVIDIA Quantum InfiniBand
      • PCIe (Peripheral Component Interconnect Express)
      • NVIDIA ConnectX InfiniBand adapters
      • NVMe (Non-Volatile Memory Express)
      • NVMe over Fabrics (NVMe-oF)
      • NVIDIA Spectrum-X
      • NVIDIA GPUDirect
      • Evaluating Modern GPU Interconnect
      • Scalable Hierarchical Aggregation and Reduction Protocol (SHARP)
      • Next-generation networking in AI environments
      • NVIDIA Collective Communications Library (NCCL)
    • Data and Memory
      • NVIDIA BlueField Data Processing Units (DPUs)
      • Remote Direct Memory Access (RDMA)
      • High Bandwidth Memory (HBM3)
      • Flash Memory
      • Model Requirements
      • Calculating GPU memory for serving LLMs
      • Transformer training costs
      • GPU Performance Optimisation
    • Libraries and Complements
      • NVIDIA Base Command
      • NVIDIA AI Enterprise
      • CUDA - NVIDIA GTC 2024 presentation
      • RAPIDs
      • RAFT
    • Vast Data Platform
      • Vast Datastore
      • Vast Database
      • Vast Data Engine
      • DASE (Disaggregated and Shared Everything)
      • Dremio and VAST Data
    • Storage
      • WEKA: A High-Performance Storage Solution for AI Workloads
      • Introduction to NVIDIA GPUDirect Storage (GDS)
        • GDS cuFile API
      • NVIDIA Magnum IO GPUDirect Storage (GDS)
      • Vectors in Memory
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Copyright Continuum Labs - 2023

On this page
  • Introduction
  • Low-Rank Adapters and Matrix Decomposition
  • Quantization and Information Loss Mitigation
  • Double Quantization and Efficiency
  • Technical Details and Training
  • Understanding Gradient Updates and Scalability
  • Hyperparameter Experimentation and Future Directions
  • Conclusion

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  1. Training
  2. The Fine Tuning Process
  3. Parameter Efficient Fine Tuning

The Magic behind Qlora

PreviousBits and BytesNextPractical Guide to LoRA: Tips and Tricks for Effective Model Adaptation

Last updated 11 months ago

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Introduction

Language Models (LMs) have revolutionised the field of natural language processing, enabling breakthroughs in tasks such as language understanding, generation, and translation.

These models, built on the transformer architecture, consist of layers with multi-head self-attention mechanisms and feed-forward neural networks. Each component has associated weight matrices that are crucial for the model's functioning and can have millions or billions of parameters in large-scale models.

Traditional fine-tuning approaches involve updating the entire weight matrix of a model, which can be computationally expensive and memory-intensive.

To address this challenge, methods like Lora (Low-Rank Adaptation) and Qlora (Quantized Lora) have emerged, focusing on updating a smaller, decomposed gradient matrix. This shift allows for more efficient training by updating specific, newly added, or adapted components instead of retraining the entire model.

Low-Rank Adapters and Matrix Decomposition

At the core of Lora and Qlora is the concept of embedded low-rank adapters, which rely on matrix decomposition.

These adapters are small neural network layers inserted between existing layers of a pre-trained model, designed to capture the essential transformations needed for a new task while keeping the complexity low. The term "low rank" refers to the fact that these adapters have fewer parameters compared to the main layers of the model.

Matrix decomposition involves representing a high-rank matrix (the weight or gradient matrix) with a lower-rank matrix (the adapter).

This process decomposes a complex, high-dimensional matrix into a simpler form that still captures the essential information. By focusing on updating the low-rank adapters instead of the entire weight matrix, Lora and Qlora enable more efficient fine-tuning of LLMs.

Quantization and Information Loss Mitigation

Qlora takes the concept of low-rank adaptation further by introducing quantization techniques, including the use of a new data type called the 4-bit normal float.

Quantization involves mapping continuous or high-precision values to a smaller set of discrete values, reducing memory usage at the cost of some information loss.

However, Qlora employs an innovative approach to mitigate this information loss.

Unlike traditional quantization methods that use a single constant, Qlora computes separate quantization constants for each block of weights. This approach ensures a more accurate and nuanced quantization, minimizing the loss of information and handling the distribution of weights more effectively.

The quantization constant represents the scaling factor used in the quantization process, where the maximum value in a vector is scaled to fit within the quantized range.

This constant is crucial for both the quantization and subsequent dequantization processes, ensuring that the original data can be closely approximated after being compressed.

Double Quantization and Efficiency

Qlora introduces the concept of double quantization, which involves quantizing not just the model parameters but also the quantization constants themselves. This approach further reduces the memory footprint, allowing for more efficient storage and processing of large models.

Compared to other parameter-efficient methods like prefix tuning and adapters, Qlora and Lora have shown superior performance, achieving comparable or better results with significantly fewer parameters. This effectiveness highlights the potential of these methods in efficiently fine-tuning LLMs.

Technical Details and Training

The implementation of Qlora involves several technical considerations to optimize performance and stability.

Model preprocessing steps, such as upcasting layer norms to float32, are employed to ensure more stable training. Memory management techniques, including the use of page optimisers and NVIDIA's unified memory, help address memory spikes that can occur when processing large mini-batches or long sequence lengths.

Gradient checkpointing is another crucial technique used in Qlora to balance memory usage and computational speed during backpropagation. By storing necessary activations for computing gradients without keeping all activations in memory, gradient checkpointing reduces the memory burden while allowing for efficient gradient computation.

Despite its advancements, Qlora maintains compatibility with standard optimization techniques like AdamW, ensuring seamless integration with existing training pipelines and optimization strategies.

Understanding Gradient Updates and Scalability

To effectively apply Qlora, it is essential for researchers and developers to understand how gradient updates work in the context of low-rank adaptation.

Visualizing these updates as modifications to a lower-rank matrix rather than the full weight matrix offers a more intuitive grasp of the process.

The scalability and reduced memory usage of Qlora make it highly applicable in industry settings. It allows for training multiple models for different tasks using a single base model, which is especially beneficial for tasks requiring frequent model updates, such as in e-commerce or recommendation systems.

Qlora's implementation is relatively straightforward, especially with tools like the Hugging Face library abstracting much of the complexity.

This simplicity enhances the accessibility of the method to a wider range of developers and researchers. Moreover, Qlora is not limited to just the largest models; it can be applied to a range of model sizes, offering efficient fine-tuning capabilities across various architectures and scales.

Hyperparameter Experimentation and Future Directions

The Qlora framework allows for experimentation with various hyperparameters, such as the number of Lora adapters, dropout rates, and layer-specific adaptations.

This flexibility enables practitioners to fine-tune models to their specific needs and constraints. The analysis suggests that the number of Lora adapters used could become a significant hyperparameter in future implementations, with the goal of finding the optimal number that fits within specific GPU memory constraints while maximizing model performance.

The rank of the low-rank matrices is another critical hyperparameter in Qlora. A lower rank means fewer trainable parameters, which can greatly reduce the computational burden. However, finding the optimal rank involves balancing efficiency and maintaining the performance of the model.

The integration of Lora and Qlora weights into the LLM is a significant design choice, involving strategically placing these weights in different parts of the model to optimise performance while maintaining efficiency.

This adaptability to various model architectures and tasks highlights the versatility of the approach.

Conclusion

Qlora represents a significant advancement in the efficient fine-tuning of language models.

By leveraging low-rank adaptation, quantization techniques, and innovative memory management strategies, Qlora enables the training of LMs with reduced computational and memory requirements.

The method's scalability, compatibility with existing optimization techniques, and strong performance compared to other parameter-efficient methods make it a promising approach for both research and industry applications.

As the field of natural language processing continues to evolve, methods like Qlora will play a crucial role in making the development and deployment of large-scale models more accessible and efficient.

Further research into hyperparameter optimisation, architectural integration, and applications across various tasks and domains will help unlock the full potential of these techniques.

The Magic behind Qlora
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