A look at this year’s crop of LoRA alternatives, including SVF, SVFT, MiLoRA, PiSSA, and LoRA-XS, all based on SVD (Singular Value Decomposition). The article compares these techniques to the original LoRA method for fine-tuning Large Language Models.
| Method | Description | Key Feature(s) | Reference |
|--------------|---------------------------------------------|---------------------------------------------|-|
| LoRA | Freezes the model and trains a small pair of low-rank “adapter” matrices. | Saves memory and compute cycles by reducing the number of trainable parameters. | arxiv.org/abs/2106.09685 |
| SVF | Uses SVD on the model’s weight matrices and fine-tunes the singular values directly. | More economical in parameters than LoRA; makes tuned models composable. | arxiv.org/abs/2501.06252v2 |
| SVFT | Adds more trainable weights on the diagonal and evaluates various alternatives. | Provides more trainable values than just the diagonal, useful for better fine-tuning. | arxiv.org/abs/2405.19597 |
| PiSSA | Tunes only the large principal values. | Designed to approximate full fine-tuning by adapting the principal singular components. | arxiv.org/abs/2404.02948 |
| MiLoRA | Tunes only the small principal values. | Retains base model’s knowledge while adapting to new tasks. | arxiv.org/abs/2406.09044 |
| LoRA-XS | Similar to PiSSA but with a slightly different mechanism. | Shows good results with significantly fewer parameters than LoRA. | arxiv.org/abs/2405.17604 |
| DoRA | Splits weights into magnitudes and directions then tunes those. | | arxiv.org/abs/2402.09353 |
| AdaLoRA | Complex mechanism for finding the best tuning rank for a given budget of trainable weights. | | arxiv.org/abs/2303.10512 |
| Method | Description | Key Feature(s) | Reference |
|--------------|---------------------------------------------|---------------------------------------------|-|
| LoRA | Freezes the model and trains a small pair of low-rank “adapter” matrices. | Saves memory and compute cycles by reducing the number of trainable parameters. | arxiv.org/abs/2106.09685 |
| SVF | Uses SVD on the model’s weight matrices and fine-tunes the singular values directly. | More economical in parameters than LoRA; makes tuned models composable. | arxiv.org/abs/2501.06252v2 |
| SVFT | Adds more trainable weights on the diagonal and evaluates various alternatives. | Provides more trainable values than just the diagonal, useful for better fine-tuning. | arxiv.org/abs/2405.19597 |
| PiSSA | Tunes only the large principal values. | Designed to approximate full fine-tuning by adapting the principal singular components. | arxiv.org/abs/2404.02948 |
| MiLoRA | Tunes only the small principal values. | Retains base model’s knowledge while adapting to new tasks. | arxiv.org/abs/2406.09044 |
| LoRA-XS | Similar to PiSSA but with a slightly different mechanism. | Shows good results with significantly fewer parameters than LoRA. | arxiv.org/abs/2405.17604 |
| DoRA | Splits weights into magnitudes and directions then tunes those. | | arxiv.org/abs/2402.09353 |
| AdaLoRA | Complex mechanism for finding the best tuning rank for a given budget of trainable weights. | | arxiv.org/abs/2303.10512 |
The article explains six essential strategies for customizing Large Language Models (LLMs) to better meet specific business needs or domain requirements. These strategies include Prompt Engineering, Decoding and Sampling Strategy, Retrieval Augmented Generation (RAG), Agent, Fine-Tuning, and Reinforcement Learning from Human Feedback (RLHF). Each strategy is described with its benefits, limitations, and implementation approaches to align LLMs with specific objectives.
This tutorial guides readers on how to fine-tune the Mistral 7B large language model using QLoRA with the Axolotl library, focusing on managing limited GPU resources for efficient training. It covers environment setup, dataset creation, configuration of QLoRA hyperparameters, the fine-tuning process, and testing the fine-tuned model.
This tutorial demonstrates how to fine-tune the Llama-2 7B Chat model for Python code generation using QLoRA, gradient checkpointing, and SFTTrainer with the Alpaca-14k dataset.
The article by Krishan Walia provides a beginner-friendly guide on fine-tuning the DeepSeek R1 model using Python. It highlights how developers can transform a general-purpose AI model into a specialized, domain-specific language model for various applications.
The article explores techniques to improve Large Language Model (LLM) accuracy, focusing on Lamini Memory Tuning. It discusses fine-tuning methods like Low-Rank Adaptation (LoRA), the advantages and disadvantages of fine-tuning, and practical steps using Lamini to achieve higher precision in SQL query generation. The author demonstrates a step-by-step approach to creating a high-quality dataset, fine-tuning, and evaluating model accuracy.
The post discusses the feasibility of fine-tuning a decoder-encoder model to translate Egyptian Middle Kingdom hieroglyphics into English. The author suggests that with sufficient training data and a tokenizer that includes Egyptian characters, the model could learn to interpret hieroglyphics fluently. Comments from users mention using plugins and existing knowledge in models as alternatives to fine-tuning.
A list of 13 open-source software for building and managing production-ready AI applications. The tools cover various aspects of AI development, including LLM tool integration, vector databases, RAG pipelines, model training and deployment, LLM routing, data pipelines, AI agent monitoring, LLM observability, and AI app development.
1. Composio - Seamless integration of tools with LLMs.
2. Weaviate - AI-native vector database for AI apps.
3. Haystack - Framework for building efficient RAG pipelines.
4. LitGPT - Pretrain, fine-tune, and deploy models at scale.
5. DsPy - Framework for programming LLMs.
6. Portkey's Gateway - Reliably route to 200+ LLMs with one API.
7. AirByte - Reliable and extensible open-source data pipeline.
8. AgentOps - Agents observability and monitoring.
9. ArizeAI's Phoenix - LLM observability and evaluation.
10. vLLM - Easy, fast, and cheap LLM serving for everyone.
11. Vercel AI SDK - Easily build AI-powered products.
12. LangGraph - Build language agents as graphs.
13. Taipy - Build AI apps in Python.
1. Composio - Seamless integration of tools with LLMs.
2. Weaviate - AI-native vector database for AI apps.
3. Haystack - Framework for building efficient RAG pipelines.
4. LitGPT - Pretrain, fine-tune, and deploy models at scale.
5. DsPy - Framework for programming LLMs.
6. Portkey's Gateway - Reliably route to 200+ LLMs with one API.
7. AirByte - Reliable and extensible open-source data pipeline.
8. AgentOps - Agents observability and monitoring.
9. ArizeAI's Phoenix - LLM observability and evaluation.
10. vLLM - Easy, fast, and cheap LLM serving for everyone.
11. Vercel AI SDK - Easily build AI-powered products.
12. LangGraph - Build language agents as graphs.
13. Taipy - Build AI apps in Python.
This article provides a comprehensive guide on fine-tuning the Llama 3.1 language model using Unsloth for efficient parameter-efficient training. It covers concepts like supervised fine-tuning, LoRA, QLoRA, and practical steps for training on a high-quality dataset.
This article provides a step-by-step guide on fine-tuning the Llama 3 language model for customer service use cases. It covers the process of data preparation, fine-tuning techniques, and the benefits of leveraging LLMs in customer service applications.
