Fine tuning

References

List of Instruct datasets

Domain adaptation models & datasets

Less Is More for Alignment

Continuous pre-training | Second stage training

QLoRA paper

HuggingFace QLora blog

HuggingFace Unsloth/TRL

Open AI FT

Gemini FT

Cohere FT

Anthropic FT practices

Amazon Bedrock Claude FT

Fine tuning & Accounting intern (An analogy)

Fine-tuning a large language model (LLM) is like training an accounting intern who not only has a strong foundation in general accounting but also possesses a vast, encyclopedic knowledge of various business operations, industries, and financial systems.

The intern, much like the LLM, has learned these things from extensive “training” (like studying global best practices, reading textbooks, and observing thousands of businesses), but they still lack the specific knowledge needed to excel within your enterprise.

Step-by-Step Analogy:

  1. Pre-trained Intern (LLM Pre-training): The intern has gone through years of academic training and internships, where they’ve absorbed general knowledge, such as:

    • Double-entry bookkeeping (fundamental language skills, common structures, grammar)
    • Tax basics and financial reporting standards (wide-ranging knowledge of global topics)
    • General software and accounting tools (the model’s understanding of a broad range of subjects)

    This is akin to a large language model being pre-trained on a vast corpus of diverse text, giving it the ability to understand and generate language based on general patterns and common knowledge.

  2. Enterprise-Specific Training (Fine-tuning): Now, when the intern joins your company, you don’t need to teach them how accounting works—they already know that. However, they don’t yet understand the specific policies and procedures that your enterprise follows. Maybe your business has:

    • Specialized tax codes (domain-specific jargon and regulations)
    • Unique financial software that your company uses (the model’s need to adapt to specific task frameworks)
    • Enterprise-specific workflow processes, like custom invoicing or auditing rules (adaptation to task-specific formats and instructions)

    Fine-tuning the intern here is like providing on-the-job training: teaching them how to apply their general accounting skills within the context of your company.

    For an LLM, this involves retraining it on your company’s specific dataset, allowing the model to learn how to best handle tasks or language structures unique to your domain.

  3. Combining General Knowledge with Enterprise-Specific Expertise: The fine-tuned intern can now handle both broad accounting concepts and your enterprise’s unique processes. They know how to approach problems by drawing on their general knowledge, but they can also tailor their solutions to fit the specific needs of the company.

    Similarly, the fine-tuned LLM combines its broad linguistic and world knowledge with specialized understanding, making it adept at handling tasks like answering domain-specific questions, generating company-specific reports, or following unique instructions.

Final Result:

Just as an intern becomes a highly valuable asset after learning your business’s specifics while retaining their general accounting knowledge, a fine-tuned LLM becomes a highly efficient tool—customized to handle the specialized tasks of your application or domain while leveraging its general knowledge to provide accurate, contextually appropriate responses.

Why Fine tune and not RAG?

Fine-tuning a large language model (LLM) is recommended over retrieval-augmented generation (RAG) in certain scenarios where the specific requirements and constraints make fine-tuning more suitable. Let’s break down the key scenarios where fine-tuning would be preferred over RAG:

1. Consistent Domain-Specific Expertise

  • Scenario: Your application needs the model to consistently generate high-quality, accurate content in a specific domain (e.g., law, medicine, finance, etc.) without relying on external sources for real-time information retrieval.
  • Why Fine-tuning: Fine-tuning allows the model to internalize domain-specific knowledge, making it inherently capable of generating accurate responses within the domain without the need for real-time lookups. Once fine-tuned, the model can perform its task offline, with reduced reliance on external data sources, ensuring consistency.
  • Example: A medical chatbot that answers detailed, technical questions about diseases and treatments based on a corpus of medical guidelines.

2. Task-Specific Adaptation

  • Scenario: You require the model to specialize in a specific task (e.g., summarization, classification, or question-answering) with a fixed set of tasks and data types.
  • Why Fine-tuning: Fine-tuning allows the model to optimize for a specific task by adjusting its internal parameters. This leads to more accurate and refined task execution, especially when dealing with highly structured tasks where consistent behavior and responses are required.
  • Example: A fine-tuned LLM for legal document summarization, trained on a large dataset of contracts, to produce consistent, accurate, and task-specific outputs.

3. Latent Knowledge Retrieval

  • Scenario: You need the model to recall information that doesn’t frequently change, like facts, definitions, or historical data, which can be baked into the model’s weights.
  • Why Fine-tuning: Fine-tuning embeds this static, long-term knowledge directly into the model’s parameters, making it faster to retrieve and ensuring that responses are internally consistent, as the knowledge is pre-learned rather than retrieved externally.
  • Example: An educational tool that explains historical events or scientific principles with stable, fixed knowledge (e.g., physics formulas, historical dates).

4. No Need for Real-Time or Dynamic Data

  • Scenario: The domain you are working with doesn’t require real-time information or updates from external databases, meaning static knowledge is sufficient.
  • Why Fine-tuning: Since there’s no need for dynamic or up-to-date information, fine-tuning is a more efficient approach. The model doesn’t need to query external sources, reducing latency and improving the speed of inference.
  • Example: A customer service chatbot for a specific company that answers questions about fixed policies, procedures, or product details.

5. Privacy and Security Concerns

  • Scenario: Your application requires heightened data privacy, and you don’t want to expose sensitive information by retrieving data from external sources (as happens in RAG).
  • Why Fine-tuning: Fine-tuning ensures that the data is embedded in the model itself without relying on third-party retrieval systems, which may pose privacy risks. Sensitive or proprietary data can be encoded directly into the model during fine-tuning, and no real-time external queries are needed, reducing potential data leakage.
  • Example: A financial advisory tool that operates entirely on a secure, private dataset of customer information without querying external sources.

6. Low-Latency or Offline Applications

  • Scenario: You need the model to generate responses with minimal latency or in environments where internet access is limited or not available.
  • Why Fine-tuning: A fine-tuned model can work entirely offline once it’s trained, without the need to access external databases or conduct internet-based retrieval operations. This is critical in situations where response time is crucial or network access is unreliable.
  • Example: An autonomous assistant in remote environments (e.g., a space mission or offline educational tool) that needs to provide responses instantly without internet access.

7. Consistency in Response

  • Scenario: You require a model that provides highly consistent responses for repeated queries or tasks, without variation due to different retrieval results.
  • Why Fine-tuning: Since the model internalizes all relevant knowledge during training, it produces more consistent outputs across multiple runs of the same query. In RAG, the responses could vary based on what documents or knowledge the retrieval system surfaces at a given moment.
  • Example: A legal assistant tool that needs to give consistent advice on specific contract clauses.

8. Resource Constraints for External Retrieval

  • Scenario: You are operating in environments where real-time retrieval of external documents is expensive, resource-intensive, or infeasible due to infrastructure limitations.
  • Why Fine-tuning: Fine-tuning eliminates the need for maintaining a separate retrieval pipeline, reducing both the complexity and costs associated with querying external data sources. The model becomes a self-contained system.
  • Example: A chatbot deployed in low-resource environments (like rural areas with limited connectivity) that answers questions based on pre-loaded knowledge.

9. Data Homogeneity

  • Scenario: The dataset you are working with is relatively homogeneous and stable, meaning it doesn’t require frequent updates or queries from external sources.
  • Why Fine-tuning: Fine-tuning is ideal when dealing with stable, well-defined, and narrow domains where the relevant data can be baked into the model. In such cases, there’s no need to constantly augment the model with external documents or changing datasets.
  • Example: An internal company assistant that provides guidance on HR policies or IT support procedures, which don’t change frequently.

10. Avoiding Complexity of Retrieval Systems

  • Scenario: You want to avoid the complexity of setting up and maintaining a retrieval system, which requires databases, search indexing, and integration with a generative model.
  • Why Fine-tuning: Fine-tuning simplifies deployment by removing the need for a sophisticated retrieval system. This approach is easier to manage and deploy, especially in simpler or resource-constrained environments.
  • Example: A small-scale personal assistant application where simplicity and low maintenance are priorities.

In Summary:

Fine-tuning is preferred when:

  • You need consistent, domain-specific expertise embedded into the model.
  • The task requires task-specific adaptation with a fixed set of objectives.
  • The knowledge is stable and doesn’t change frequently (e.g., static, factual knowledge).
  • Real-time data retrieval is unnecessary or infeasible.
  • There are privacy or latency concerns, or the system needs to operate offline.
  • You need to avoid the complexity of managing an external retrieval system.

In contrast, RAG is better suited when real-time, up-to-date, or domain-specific information is crucial, and the model needs to augment its responses with dynamically retrieved data.