An Overview of Generative AI Model Fine Tuning

13 min readMar 3, 2024

Author: Jack Song

Imagine a high school junior, with just a laptop, enhancing an open-source 7B Large Language Model (LLM) to produce smarter responses in merely 15 to 30 minutes. Sounds like science fiction? It’s not — it’s entirely possible today. However, does this accessibility imply that fine-tuning Generative AI models is a simple, entry-level task? Far from it.

In this exploration, we’ll dive into the complex world of industry-grade Generative AI Model Fine-Tuning. We’ll uncover the vital skills, knowledge, and engineering prowess required, alongside introducing a burgeoning role within AI: the Generative AI Fine Tuner.

What is Industry-Grade GenAI Model Fine-Tuning?

At its core, fine-tuning involves enhancing pre-trained open-source foundation models. This process can include domain-specific pretraining (optional), instruction fine-tuning, and Reinforcement Learning with Human or AI Feedback (RLHF/RLAIF). These models, often referred to as Large Foundation Models (LFMs), leverage textual or multi-modal datasets — encompassing visual and video data — tailored for specific or multiple domain tasks. For simplicity, we’ll refer to these comprehensive models as LFMs, with Large Language Models (LLMs) being a subset.

The Crucial Phases of LFM Fine-Tuning

Fine-tuning a Generative AI model in an industry setting is a nuanced process, involving several key phases and steps. These are part of the daily grind for Generative AI Fine Tuners and depend on the ultimate goal: whether the fine-tuning aims to enhance the model for multiple domain tasks or a specific task. When targeting multiple domains, pre-training becomes a valuable step, though it’s not necessary for singular task enhancements.

Phase 1: Meticulous Dataset Preparation

Fine-tuning Generative AI models is akin to preparing a gourmet meal — the quality of the ingredients, or in this case, datasets, dictates the success of the outcome. This phase is foundational, requiring a blend of art and science to curate and prepare datasets that will train the models to perform with precision and relevance across various domains.

1. Pre-training Datasets (Optional)

Crafting a pre-training dataset is an art form. It often encompasses a billion tokens, curated from domain-specific large text corpora, embodying billions of documents meticulously cleaned and filtered. This process demands an advanced understanding of data engineering, ensuring the dataset is free from duplications and irrelevant information, tailored for domains like online travel, finance, and more.

2. Instruction Datasets

The backbone of fine-tuning, instruction datasets, are expansive collections designed to bolster the model’s capabilities. These datasets are derived from a myriad of sources, including state-of-the-art model providers like GPT-3.5 and GPT-4, across a vast spectrum of tasks. A rigorous deduplication and filtering process ensures the focus remains on domain-specific instructions, enhancing the model’s accuracy and relevance.

3. Human & AI Feedback Datasets

Human feedback has traditionally been the gold standard for aligning LFMs with desired outcomes. However, the challenge of acquiring high-quality human feedback — requiring extensive labor and subjective judgment — has led to the integration of AI-assisted feedback. This approach leverages AI to refine domain-specific reasoning instructions, producing “chosen” responses from high-performance models and “rejected” responses from lower-tier models. The result is a rich dataset, albeit smaller in size, that fine-tunes the model’s decision-making capabilities.

4. Visual Instruction Datasets (Optional)

For LFMs tasked with understanding and generating multi-modal content, visual instruction datasets become imperative. Utilizing open-source visual understanding models, such as LLava, these datasets enhance the model’s ability to interpret and generate complex images and instructions, bridging the gap between textual and visual information seamlessly.

5. Downstream Evaluation Datasets

Benchmarking the performance of LFMs requires a diverse set of downstream datasets, each tailored to specific tasks like chart understanding, sentiment analysis, named entity recognition, and more. This task-driven curation process ensures that the model’s performance can be accurately measured across a wide range of applications, providing clear insights into its strengths and areas for improvement.

Phase 2: Navigating the Maze of Foundation Model Selection

Selecting the optimal foundation model for fine-tuning is akin to charting a course through a rapidly evolving landscape. The decision is pivotal, akin to choosing the right canvas and palette for a masterpiece painting. This phase is characterized by exploration, experimentation, and strategic selection, ensuring the chosen model aligns perfectly with your domain-specific requirements.

The Challenge of Choice

In the vast expanse of open-source foundation models, making a choice can seem daunting. With a plethora of models vying for attention and new contenders emerging at a breakneck pace, the task is anything but straightforward. Leaderboards and benchmarks offer a snapshot of performance, but they are mere signposts in a journey that demands hands-on evaluation. The truth is, there’s no one-size-fits-all solution; the efficacy of a model is best determined through direct application to your specific tasks.

Starting Points and Practical Recommendations

Despite the absence of a universal guide, beginning with models that enjoy a “popular high reputation” can offer a solid starting point. Recent favorites among fine tuners, such as the Mistral and LLAMA 2 series, have proven their mettle across various domains. However, a nuanced approach is recommended: opt for the base version of these models rather than their instruction-specific variants. For instance, choosing Mistral-7B-v0.1 over Mistral-7B-instruct-v0.1 as your foundation allows for a more flexible and adaptable fine-tuning process.

Phase 3: The Art of Domain-Specific Pretraining (Optional)

Venturing into domain-specific pretraining is akin to fine-tuning a high-performance engine for a specific race track. It’s an optional yet potent phase in the fine-tuning process, requiring not just a keen understanding of the model and its capabilities but also a mastery of advanced training techniques and a strategic approach to dataset integration.

The High Stakes of Pretraining

Pretraining a foundation model with your domain-specific billion-token datasets is a Herculean task. It demands vast computing resources, sophisticated distributed training techniques, and a cost-effective AI infrastructure. Only a handful of fine tuners venture into these waters, guided by their expertise in handling large-scale datasets and their proficiency in leveraging state-of-the-art AI training optimizations.

Essential Skills and Knowledge for Fine Tuners

To navigate this complex landscape, fine tuners must arm themselves with a deep understanding of various Transformer models (such as Mistral AI, Llama 2, Zephyr, Yi, and LLaVa) and be versed in groundbreaking LFM training optimizations like Flash Attention 2. They must also be adept at:

Employing extended sequence lengths to accommodate lengthy documents.
Utilizing techniques like LoRA (Low-Rank Adaptation) for efficient pretraining and fine-tuning.
Mastering parameter tuning specific to model architecture and training tasks.
Accelerating the training process with tools like DeepSpeed for multi-GPU setups and Ray for Multi-Machine Training (MMT).

Incorporating LoRA adapters into a small foundation model, for example, Mistral 7B, can significantly enhance training efficiency but demands a delicate balance to avoid overwhelming the model’s generalization capabilities.

Navigating the Pitfalls

Despite the potential gains, domain-specific pretraining is fraught with “mystery traps” that can derail the process:

Corpora Contamination: Overloading the model with large domain-specific corpora may disrupt the original distribution, diminishing its generalization ability.
Increased Hallucinations: An uptick in inaccuracies post-pretraining.
Inefficient Use of LoRA: Applying LoRA indiscriminately can hamper efficiency and affect generalization.
Overtraining: Exceeding one epoch may not always yield performance benefits.

Experienced fine tuners develop strategies to mitigate these risks, such as blending the base model’s original training datasets with domain-specific data, even incorporating broad datasets like the C4 dataset to maintain a balance.

Phase 4: Elevating AI with Precision Prompting and Instruction Tuning

In the intricate dance of fine-tuning Generative AI models, this phase shines as a beacon, guiding us through the nuanced practices of prompt engineering and instruction tuning. This phase is pivotal, sharpening the model’s ability to dissect and respond to complex inquiries with precision and relevance.

Crafting Intelligent Prompts

Prompt engineering transforms the art of question-asking into a science, utilizing strategic methods to evoke the most insightful responses from the AI. This involves:

Leveraging Expertise: Collaborating with domain experts to craft questions that define clear, actionable tasks for the AI, setting a high bar for its performance.
Guided Thought Process: Structuring questions to lead the AI through a logical sequence, ensuring it considers all necessary information before responding.
Comprehensive Background: Assembling all pertinent details related to the inquiry, equipping the AI with a full understanding of the context and specifics of the task.
Focused Task Queries: Directing the AI’s vast knowledge towards specific challenges, demanding its specialized reasoning and insights.
Structured Prompting: Implementing templates or formats that help the AI navigate through complex topics systematically, ensuring focused and relevant outputs.
Boundary Setting: Applying constraints to refine the AI’s responses, ensuring accuracy and relevance to the query at hand.

Refining Instructions for Peak Performance

The concept of instruction tuning emerged from the observation that large language models, despite their vast knowledge and language understanding capabilities, often struggle to follow specific instructions or to perform consistently across different types of tasks. Early efforts in instruction tuning involved manually curating datasets with instructions and corresponding outputs, leveraging the models’ ability to generalize from these examples. Pioneering large “instruction-tuned” language models (i.e., instructGPT, ChatGPT) have demonstrated a remarkable ability to generalize zero-shot to new tasks. Nevertheless, they depend heavily on human-written instruction data that is often limited in quantity, diversity, and creativity, therefore hindering the generality of the tuned model.

Self-instruction tuning is a more recent innovation and was considerably spread by Stanford Alpaca, building on a framework for improving the instruction-following capabilities of pre-trained language models by bootstrapping off their generations. In SIT, the model generates instructions or explanations for a given task and then attempts to solve the task based on these self-generated instructions.

Key strategies include:

Advanced QLoRA Techniques: Enhancing instruction tuning across all linear layers for a performance nearing full fine-tuning, utilizing state-of-the-art optimizations to minimize accuracy compromises.
AI Feedback Alignment: Using direct preference optimization (DPO) to fine-tune the model’s responsiveness to prompts, improving its natural language understanding without relying on reward models.
Distilled DPO (dDPO): A cutting-edge approach that combines LoRA and DPO objectives, refining the model’s precision and relevance in responding to complex prompts.

Expanding Horizons with Multimodal Instruction Tuning (Optional)

For tasks that span beyond text to include visual data, integrating visual models like CLIP enhances the AI’s understanding, converting image inputs into text embeddings to feed into the LFM, broadening its comprehension across modalities.

Integrating External Tools and Calls

Surmounting the limitations of open-source foundation models in processing complex mathematical or domain-specific information involves linking them with external tools or functions. This integration allows the AI to excel in its core competencies of understanding and generating contextually rich content.

Harnessing Retrieval Augmented Generation (RAG)

RAG empowers LFMs to access and incorporate additional, relevant information on demand. This capability is crucial for addressing queries beyond the model’s initial training scope, enriching responses with up-to-date and precise data from proprietary sources, and significantly reducing inaccuracies.

Phase 5: Perfecting AI Through Offline Experiments, Evaluation, and Implementing Guardrails

This phase marks the culmination of the fine-tuning process, focusing on rigorous offline experiments, detailed evaluations, and the establishment of guardrails to ensure the model’s reliability, safety, and ethical compliance. This phase is pivotal for validating the model’s effectiveness and readiness for real-world applications.

Conducting Thorough Offline Experiments

Fine tuners orchestrate a series of experiments to test the model’s performance across various tasks, paying special attention to critical indexes like hallucination rates. These experiments are designed to challenge the model, identifying strengths and pinpointing areas for improvement. Typically, three model variants are assessed:

INST Version: The initial instruction-tuned model, refined for clarity and focus.
DPO Version: Enhanced with AI feedback via direct preference optimization, building on the INST model.
T&R Version: The culmination of tuning, incorporating tools/API integration, and Retrieval Augmented Generation (RAG) for a comprehensive capability.

Domain-Specific Evaluations: Bridging AutoEval and HumanEval

Evaluating the model’s domain-specific performance requires both automated tools and human expertise. Fine tuners develop benchmarks tailored to the domain, encompassing real-world questions and scenarios. This dual approach ensures a balanced assessment:

AutoEval: Provides scalable, quantitative feedback on the model’s performance, efficiently comparing it against benchmarks.
HumanEval: Adds depth to the evaluation with expert reviews, ensuring the model’s responses are not just accurate but also contextually relevant and coherent.

Implementing and Enhancing Guardrails

Guardrails are critical for ensuring the model operates within ethical, safety, and privacy boundaries. A significant focus within this is on mitigating hallucinations — a prevalent concern where models may generate incorrect or misleading information.

Hallucination Checks and Mitigation Strategies

Addressing hallucinations requires a multifaceted approach:

Quantitative Measures: Fine tuners assess the extent of hallucinations, particularly in how models handle definitions of domain-specific terms. This involves creating a hallucination index (HI) — the ratio of correctly generated definitions by the model, providing a clear metric for evaluation.
Human Judges: Experts with deep domain knowledge review model responses, identifying and labeling hallucinations. This human insight is invaluable for understanding the nuances of model-generated errors.
Mitigation Techniques: Adoption of specific strategies within the fine-tuning process to reduce hallucinations, ensuring the model’s outputs are accurate and trustworthy.

Ensuring Broad Guardrail Coverage

Beyond hallucinations, guardrails span across multiple dimensions to ensure comprehensive safety and ethical compliance:

Bias Reduction: Identifying and mitigating biases to prevent inequality amplification.
Privacy Protection: Safeguarding against the unintentional disclosure of sensitive information.
Content Safety: Implementing filters to block harmful or inappropriate content generation.
Misuse Prevention: Creating systems to deter unethical applications of the technology.
Ethical Compliance: Aligning model outputs with societal values and ethical standards, ensuring outputs reflect fairness, respect, and accountability.

Leveraging LLM Observability for Continuous Improvement

Observability into the model’s operations enhances the effectiveness of guardrails, involving:

Metrics Collection: Gathering comprehensive data on model interactions for analysis.
Analysis Tools: Utilizing advanced tools to detect anomalies, biases, and other concerns highlighted by guardrails.
Human-In-The-Loop: Incorporating expert judgment for nuanced evaluations, particularly in ethical considerations and content safety.
Feedback Loops: Creating mechanisms to integrate insights back into training and tuning processes, fostering continuous refinement of the model and its guardrails.

Phase 6: Launching into the Future — Deployment and Beyond

This phase represents the culmination of the fine-tuning journey for Generative AI models, transitioning from the meticulous preparation and evaluation stages to real-world application. This final phase is dedicated to deploying the model into production, ensuring its seamless operation, and establishing a framework for ongoing improvement. It’s about making sure that the model not only meets but exceeds expectations from the outset and continues to evolve in alignment with changing needs and landscapes.

Deployment for Inference

Model Serving: The deployment process begins by establishing the model on a robust and scalable infrastructure, which is crucial for effectively handling real-time or batch inference requests. This step involves carefully selecting hardware and software to meet performance benchmarks, including load handling and response times. Additionally, the integration of Retrieval Augmented Generation (RAG) is pivotal in enhancing model-serving capabilities. RAG empowers Large Language Models (LLMs) to access and incorporate additional, relevant information on demand, extending the model’s ability to address queries beyond its initial training scope. This augmentation is essential for enriching responses with up-to-date and precise data from proprietary sources, significantly reducing inaccuracies and ensuring that the model’s outputs remain relevant and accurate.

API Integration: To weave the model’s capabilities into the fabric of existing systems and workflows, APIs act as the bridge. Crafting well-documented, easily navigable, and version-controlled APIs is essential for a smooth integration process. These APIs also need to be fortified with solid error-handling mechanisms to ensure reliability.

Online Experiments and Monitoring

A/B Testing: A prudent approach to deployment involves A/B testing, pitting the fine-tuned model against previous iterations or other baselines. This comparative analysis is invaluable for gauging the model’s impact on user experience and system efficiency, guiding further refinements.

Real-time Monitoring: The deployment phase extends into continuous oversight, leveraging monitoring tools to track the model’s performance, operational metrics, and overall health. Anomalies or dips in performance trigger alerts, prompting immediate attention.

Iterative Improvement

Update and Retrain: Insights gleaned from ongoing monitoring and user feedback form the basis for the model’s continuous evolution. Updating and retraining become periodic necessities to adapt to new data, emerging user requirements, and shifts in context. This cycle of refinement ensures the model’s sustained relevance and effectiveness.

Essential Skills Matrix for Generative AI Fine Tuners

Transitioning into the realm of Generative AI requires a blend of traditional machine learning skills and new competencies unique to the complexities and nuances of fine-tuning Generative AI models. This transition marks a significant evolution from the skill set of a classical machine learning engineer to that of an industry-grade Generative AI Fine Tuner — a role that perhaps only a tiny fraction of MLEs are currently equipped to navigate.

Below is a matrix that outlines the essential skills across different phases of the Generative AI model fine-tuning process, highlighting both traditional and emerging competencies required to excel in this evolving field.

Interpretation of the Matrix:

High: Indicates areas where a deep understanding or significant experience is essential.
New: Highlights emerging skills or knowledge areas that are increasingly important for Generative AI Fine Tuners, distinguishing them from traditional MLE roles.

Key Insights:

Data Engineering & AI Labeling: Foundational for preparing datasets, with new labeling techniques specific to Generative AI emerging.
Model Architecture & Selection: Understanding the intricacies of different Generative AI models is still important, even though most of them were defined in “Transformer”, with a new emphasis on selecting and optimizing models for specific tasks.
Prompt Engineering & Instruction Tuning: Specialized skills that are central to refining how models interpret and respond to input, requiring innovative approaches.
Distributed Training: A heightened focus on managing and executing training processes across distributed systems to handle the scale of data and computation.
Auto & Human Eval: Essential for assessing model performance, with a blend of automated tools and human judgment to evaluate nuanced responses.
Deployment & Inference: This skill set pertains to the operationalization of models, focusing on their capability to meet and adapt to real-world applications and demands. It includes the integration of advanced techniques such as Embedding Based Retrieval and Retrieval Augmented Generation (RAG) to ensure that the models can dynamically incorporate relevant information and continuously update their knowledge base with fresh data.
Guardrails & Observability: New competencies around implementing safety, ethical standards, and monitoring model behavior in live environments.