Fine‑tuning a large language model (LLM) involves continuing the training of a pre‑trained model on domain‑specific data to adapt its behavior to a particular task or organization. While pre‑training teaches a model general language understanding, fine‑tuning refines those abilities to produce consistent, context‑appropriate outputs. In 2025, with smaller open‑weight models like Meta’s Llama 3 and Google’s Gemma offering impressive baseline capabilities, many teams wonder whether additional training is still necessary. The answer is nuanced: prompt engineering and retrieval‑augmented generation (RAG) can temporarily steer a model, but only fine‑tuning delivers persistent behavior change that aligns outputs with an organization’s tone and domain.
Before diving deeper, it’s important to summarize the landscape:
Andrej Karpathy, former director of AI at Tesla, advises that high‑quality data matters more than data quantity: “It is better to have a small, well‑curated dataset than a large noisy one.” This philosophy underpins effective fine‑tuning.
Fine‑tuning remains relevant and powerful in 2025 because it instills lasting domain expertise into an LLM. Understanding its benefits, limitations, and how it compares to other methods provides the foundation for the rest of this guide.
Enterprises adopt generative AI to automate workflows, improve customer interactions and extract insights from data. However, off‑the‑shelf LLMs rarely match a company’s tone, terminology, or compliance requirements. Fine‑tuning addresses these gaps by aligning models with specific use cases, boosting return on investment (ROI) and providing competitive differentiation.
Fine‑tuning introduces additional training costs and requires expertise, yet it often proves cost‑effective over time. Parameter‑efficient methods (discussed later) lower hardware requirements and training time. The ROI stems from increased task accuracy, faster onboarding of domain‑specific features and reduced reliance on external retrieval systems. Companies like Clarifai leverage compute orchestration to streamline the fine‑tuning pipeline—balancing costs and performance by automatically assigning GPU resources and scheduling jobs.
Yaoxin Zhai, head of AI at a Fortune 500 healthcare provider, notes that fine‑tuning improved the accuracy of their medical coding assistant by 30 % while ensuring that the assistant adhered to HIPAA privacy rules. The cost of training was offset within months by reduced manual review time.
Enterprises fine‑tune LLMs when they require domain‑specific knowledge, consistent brand voice, or latency improvements. When done thoughtfully, the benefits outweigh the costs, especially with parameter‑efficient techniques and orchestration platforms like Clarifai.
Pre‑training exposes an LLM to massive corpora to learn grammar, facts and world knowledge. Fine‑tuning continues training on smaller, task‑specific datasets, updating the model weights to specialize its behavior. Pre‑training is expensive and typically performed by large labs; fine‑tuning can be executed on commodity GPUs, especially when using parameter‑efficient methods.
Prompt engineering involves crafting instructions to steer the model without altering its weights. It’s useful for quick experiments and tasks where the model needs to adapt to many contexts, but it offers transient control—the model reverts to its original behavior in new sessions. Fine‑tuning, by contrast, permanently shifts the model’s output distribution.
RAG injects external context by retrieving relevant documents from a knowledge base at inference time. This approach excels for tasks requiring up‑to‑date information but comes with additional latency and infrastructure requirements. Fine‑tuning is often complementary to RAG: use RAG for dynamic content and fine‑tuning for stable, domain‑specific patterns. According to a 2024 analysis, parameter‑efficient fine‑tuned models sometimes outperform RAG for repetitive domain tasks because the knowledge is internalized.
Sophisticated workflows combine these techniques: you might fine‑tune a base model to learn domain syntax and safety, then augment it with RAG to access recent documents. You may also use prompt tuning or soft prompting to further steer behavior; these techniques create trainable prompt embeddings without modifying the core model.
Sébastien Bubeck from Microsoft Research suggests viewing these methods as tools on a spectrum: “Fine‑tuning internalizes knowledge; RAG references external knowledge; prompt engineering shapes behavior within the model’s current knowledge.” Combining them strategically yields robust AI systems.
Fine‑tuning alters the model weights for long‑term specialization, whereas pre‑training builds general intelligence, prompt engineering steers behavior transiently and RAG injects external information. A hybrid approach often delivers the best results.
Fine‑tuning strategies have diversified, offering options that balance control, compute cost, and task performance.
Full fine‑tuning retrains all the model parameters. It offers maximum control but demands significant compute and large task‑specific datasets. For example, fine‑tuning a 70 billion‑parameter model like Llama 2 70B requires powerful GPUs and careful hyperparameter tuning. The trade‑offs include risk of overfitting, catastrophic forgetting (overwriting general knowledge), and high energy usage.
PEFT techniques update only a subset of model parameters, reducing memory and compute requirements. Common methods include LoRA (Low‑Rank Adaptation), QLoRA (Quantized LoRA) and adapter layers.
Instruction tuning trains the model on a dataset of instruction–response pairs to improve its ability to follow instructions. It often pairs with reinforcement learning from human feedback (RLHF), where a reward model scores outputs and guides further training. These methods enhance general helpfulness and alignment but require curated datasets and human feedback, making them resource‑intensive.
Inspired by cognitive science, system‑2 fine‑tuning encourages models to reason more deeply by integrating reflective self‑questioning. Recent research introduced the New News dataset, showing that self‑QA protocols help models consolidate new information into their weights. This method aims to improve systematic reasoning, planning and multi‑hop inference. It is still experimental but signals a shift toward cognitive‑style training.
Prompt tuning learns continuous prompt embeddings that steer model outputs without modifying the base model. Soft prompting operates at the embedding level to encode instructions. These techniques are quick and low‑cost but provide limited control compared with weight‑based tuning.
Harrison Kinsley, co‑creator of Pytorch tutorials, suggests starting with LoRA for most projects: “LoRA and QLoRA bring fine‑tuning to consumer GPUs, making it accessible without sacrificing too much performance.” He cautions that full fine‑tuning should be reserved for cases with large budgets and critical accuracy requirements.
The fine‑tuning landscape spans full, PEFT, instruction tuning, RLHF, system‑2, prompt tuning, and hybrid approaches. Parameter‑efficient methods like LoRA, QLoRA, and adapters strike a balance between performance and cost. Emerging techniques such as system‑2 show promise for improving reasoning.
Model Training in machine learning is about building the best mathematical representation of the relationship between data features and target labels. For the models to perform consistently, you need to understand each model, and find the right data fit, and keep tweaking it to find the best combination of weights and biases for the model.
The Clarifai platform makes it easy to build AI models for your own business solutions. Whether you want to create your own model, fine-tune an existing one, or get started right away by using one of the pre-trained models from the community, the platform provides a user-friendly experience for all your AI needs.
Let's explore 8 valuable tips for training and fine-tuning machine learning models on the Clarifai platform.
First, let's begin by exploring various possible ways to add a model.
You have four different options to add and use a model:
Clarifai offers a variety of powerful model types, each designed to generate meaningful outputs based on specific inputs. Whether you're working with images, videos, or text, there's a perfect model type for your needs.
Below, you can see different types of models we offer for image data, such as Transfer Learn, Visual Classifier, Visual Detector, Visual Segmenter, Visual Anomaly, Visual Embedder, and Clusterer. For a detailed look at these model points, check our documentation here.
Transfer Learning Classifier: Utilizes a pre-trained model to classify images or texts, adapting to new tasks with minimal training data. Ideal for applications needing quick adaptation to new classification tasks without extensive data or computational resources.
Visual Classifier: Classifies images and video frames into predefined categories or concepts. Useful for categorizing visual content in applications like photo organization, content moderation, or retail product identification.
Visual Detector: Detects and locates objects within images or video frames, providing bounding box coordinates and classifications. Employed in surveillance, quality inspection, or augmented reality for identifying and tracking objects in real-time.
Visual Segmenter: Performs pixel-level segmentation in images, identifying and classifying detailed regions or objects. Essential for detailed image analysis in medical imaging, autonomous vehicles, or precision agriculture.
Visual Anomaly: Detects anomalies in visual data, providing an image-level score and localized anomaly heatmap. Applied in industrial inspection, quality control, or security to identify unusual or defective items.
Visual Embedder: Converts images and video frames into high-level vector embeddings for advanced visual understanding. Facilitates visual search and similarity analysis in e-commerce, digital asset management, or recommendation systems.
Clusterer: Clusters visually or semantically similar images and video frames in an embedding space. Ideal for organizing large visual datasets, enhancing visual search capabilities, or providing insights without explicit labeling.
By selecting the right model type, you can train or perform transfer learning on models for your own use cases.
Let’s look at one of the model types, Transfer Learn.
Transfer Learn is one of the model types that you can use to classify images or texts based on the embedding model that has indexed into your Clarifai app.
Transfer learning leverages feature representations from a pre-trained model based on massive amounts of data, so you don't have to train a new model from scratch and can quickly learn new things with minimal training data.
To train a Transfer Learn Classifier, all you need to add is a Model Id, Training dataset, select the base embedding model, and specify the concepts that you want the model to predict. Check our blog for a detailed discussion of the concept of transfer learning, and check out this video to learn more about transfer learning on large language models (LLMs):
While you can use pre-built models to help you create AI solutions quickly and efficiently, there are many cases where accuracy and the ability to carefully target solutions takes priority over speed and ease of use.
For such cases, the option is to deep fine-tune your custom models. You can take advantage of a variety of templates that Clarifai offers when building your deep fine-tuned models.
Templates give you the control to choose the specific architecture used by your neural network, and also define a set of hyperparameters that you can use to fine-tune.
To name a few there are Visual Classification, Visual Detection, Text Fine-Tuning Templates and others.
Learn more about Deep Fine-Tuning templates here.
Agent system operators are fixed-function operators that are non-trainable. They help you connect and direct your models within a workflow.
These operators can be chained together with models to automate tasks. Below, you can find different operators, such as a prompter, which is a pre-configured text used to instruct LLMs, an image cropper used to crop the input image according to each input region, and others.
Check out various operators and learn how to integrate them into your workflow here:
Developing the best-performing machine learning models involves a lot of iterative work, as you may need to adjust hyperparameters, training data, or other parameters. Maintaining a history of these changes over time can assist you in reaching the objectives you initially envisioned for your machine learning models.
The Clarifai Portal allows you to track and manage different versions of your model. Utilizing the Portal for model version control can help you achieve several things, including versioned reproducibility, better collaboration and improved troubleshooting.
Once you have successfully trained the model, you may want to test its performance before deploying it in a production environment.
The model evaluation tools in the platform allows you to perform cross-validation on a specified model version. Once the evaluation is complete, you can view the various metrics that provide insights into the model’s performance.
You can run your model predictions directly on the inputs using the Clarifai portal. After uploading the input via the portal, the model will analyze it and provide predictions.
As mentioned earlier, for the machine learning models to work well, it's important to understand each model's parameters and find the right data fit. The Clarifai platform makes this easier by providing various model types, deep fine-tuning templates, agent system operators, version management, and evaluation tools. This allows users to easily integrate AI into their business solutions.
High‑quality examples matter more than large volumes. A small curated dataset yields better generalization than a massive but noisy one. Follow Karpathy’s advice: prioritize quality over quantity.
When real data is scarce, generate synthetic examples using LLMs or data augmentation. Tools like Distilabel produce high‑quality synthetic data, but validate synthetic outputs carefully to avoid encoding errors. Combining synthetic and real data can improve robustness.
For sensitive domains, follow legal frameworks such as GDPR and HIPAA. De‑identify personal information and store data securely. When using local hardware, Clarifai’s Local Runners enable fine‑tuning within secure environments—data never leaves the organization.
Emily Bender, linguistics professor and AI ethics advocate, warns that training on biased data amplifies biases: “Bias in, bias out.” She advises auditing datasets for representativeness and fairness.
Effective fine‑tuning begins with clean, accurate, and fairly labeled data. Prioritize quality over quantity, consider synthetic augmentation, and respect privacy laws. Clarifai’s data tools can assist with labeling, balancing and secure storage.
Evaluating fine‑tuned models requires a mix of quantitative metrics, qualitative assessments, and human judgment.
Because automatic metrics don’t capture nuances such as helpfulness, tone or factual correctness, human raters are essential. Use double‑blinded evaluation to reduce bias and include criteria like clarity, relevance, conciseness, and ethical considerations.
Test models on adversarial inputs and edge cases. Evaluate fairness across demographic groups to detect biases. Tools like LM Evaluation Harness, trix and Open LLM Leaderboard facilitate comparative evaluations.
After deployment, track performance drift and error patterns. Establish thresholds for re‑training or rolling back models. Clarifai’s monitoring workflows can trigger alerts when metrics deviate from acceptable ranges.
Yoshua Bengio, Turing Award laureate, notes that “metrics should align with human values and the intended use.” He advocates combining automated evaluation with user feedback to capture real‑world impact.
No single metric suffices. Combine accuracy, BLEU/ROUGE, perplexity, human evaluation and robustness testing. Continuous monitoring ensures models remain reliable and fair in production.
Fine‑tuning demands hardware ranging from consumer GPUs (for QLoRA) to multi‑GPU clusters (for full fine‑tuning). LoRA and QLoRA reduce the number of trainable parameters, enabling models with billions of parameters to be fine‑tuned on commodity GPUs. The MLCommons task force selected Llama 2 70B as the benchmark model for fine‑tuning because it balances size and community adoption.
Managing compute resources efficiently is crucial. Clarifai’s compute orchestration automates GPU scheduling, distributes workloads across clusters, and monitors utilization. It also integrates with Axolotl, DeepSpeed, and other frameworks to optimize memory usage and throughput. On‑premises Local Runners allow secure fine‑tuning where data cannot leave the network, fulfilling compliance requirements.
Large‑scale fine‑tuning consumes substantial energy. Parameter‑efficient methods mitigate environmental impact. Choosing efficient hardware and training during off‑peak hours can further reduce energy footprints.
Lin Qiao, co‑founder of Clarifai and head of compute infrastructure, explains that right‑sizing infrastructure is critical: “Don’t spin up a TPU pod when a LoRA adapter will do”. Start with smaller models and scale up only when necessary.
Plan your fine‑tuning infrastructure carefully. Use LoRA/QLoRA for resource efficiency, and adopt orchestration platforms like Clarifai to manage compute costs. Balance cloud flexibility with on‑premises security and consider environmental impacts.
Despite its power, fine‑tuning carries several risks that must be managed.
Training on small or biased datasets can cause overfitting, where the model memorizes training examples and fails to generalize. Catastrophic forgetting occurs when fine‑tuning overwrites general knowledge learned during pre‑training, reducing versatility. Mitigation strategies include using PEFT, early stopping, regularization, and periodically mixing in examples from the base model’s data distribution.
Recent research shows that fine‑tuning can erode safety guardrails, even when the data contains no harmful content. Malicious actors can exploit this by fine‑tuning models to circumvent safety systems. To address this, run safety evaluations and monitor for harmful outputs. Approaches such as Safe Delta and other post‑fine‑tuning alignment methods are emerging.
Fine‑tuning on unbalanced datasets can amplify social biases. Audit your data for representation and conduct fairness evaluations across demographic subgroups. Use bias mitigation techniques such as re‑sampling and adversarial debiasing.
Fine‑tuned models can unintentionally memorize and leak sensitive data, especially when trained on personal information. Use differential privacy techniques, secure computing environments and avoid training on identifiable data. Clarifai’s Local Runners ensure data remains within secure boundaries.
Large models require significant compute power, increasing costs and environmental impact. Parameter‑efficient methods reduce this footprint, but organizations must still monitor energy usage and consider sustainability goals.
Percy Liang, director of Stanford’s Center for Research on Foundation Models, advocates transparency in fine‑tuning: “Publish your data sources, training procedure and evaluation results. Openness is key to trust.”
Fine‑tuning introduces risks—overfitting, forgetting, safety degradation, bias, privacy issues and high compute costs. Mitigate them through careful data curation, safety evaluations, bias audits, differential privacy and efficient training techniques.
In healthcare, fine‑tuned models assist with medical summarization, clinical note coding, and patient triage. For instance, researchers fine‑tuned smaller models to scan medical records and detect social determinants of health, outperforming larger models trained on general data. When data privacy is essential, organizations use Local Runners to fine‑tune models within secure environments.
Banks and fintech companies fine‑tune LLMs for regulatory compliance, risk analysis, and fraud detection. Fine‑tuned models can extract key information from lengthy regulations and generate compliance summaries. Combined with RAG, they reference updated regulations while maintaining domain‑specific language. Clarifai’s compute orchestration helps these institutions manage large training runs efficiently.
Fine‑tuned chatbots provide personalized shopping experiences, adapt to brand tone and generate product descriptions. They integrate with inventory systems to deliver up‑to‑date recommendations. Parameter‑efficient tuning methods like adapter layers allow quick customization per product category.
Educational platforms use fine‑tuned models to provide personalized tutoring and adaptive assessment. For example, a model fine‑tuned on math curriculum content can answer students’ questions with consistent pedagogy and generate step‑by‑step explanations.
Governments and law firms apply fine‑tuning for legal document summarization, policy analysis and legislative drafting. A recent benchmark for MLPerf fine‑tuning uses the SCROLLS dataset—government reports summarizing research—to evaluate LLM performance on long‑form documents.
Fei‑Fei Li, Stanford AI Lab co‑director, notes that domain‑specific fine‑tuning democratizes AI: “We see smaller organizations using LoRA and Local Runners to tailor LLMs to niche domains—from agriculture to microfinance.”
Fine‑tuned LLMs power healthcare automation, financial compliance, personalized retail, education, government analytics, and many other verticals. Domain‑specific training yields higher accuracy and better user experiences than general models.
Researchers are developing LoRA 2.0, an evolution of LoRA that introduces hierarchical low‑rank matrices and dynamic rank allocation, further reducing trainable parameters and improving performance. In combination with spectrum and QLoRA, LoRA 2.0 aims to achieve near‑full‑fine‑tuning quality at a fraction of the cost. Watch for integration into frameworks like Axolotl and TorchTune.
Future LLMs will incorporate text, images, audio and video. Fine‑tuning these multimodal models requires aligning across modalities—e.g., training a model to generate captioned images or transcribe medical notes. Parameter‑efficient methods extend to vision and audio adapters, enabling cross‑modal adaptation.
Cognitive‑inspired approaches like system‑2 fine‑tuning encourage reflection and structured reasoning. The New News dataset demonstrates that self‑questioning protocols improve how models internalize new knowledge. Future research may combine system‑2 fine‑tuning with self‑consistency checks to reduce hallucinations and improve factuality.
Leveraging LLMs to generate high‑quality synthetic training data will become more sophisticated. Techniques like self‑play and scenario simulation can create diverse edge cases for training safety‑critical systems. For example, generating hypothetical medical emergencies to fine‑tune triage models or simulating legal cases to train legal AI assistants.
To preserve privacy, researchers are exploring differentially private fine‑tuning and federated learning where models are trained across decentralized datasets without centralizing sensitive information. This is especially relevant for healthcare and finance.
Rather than one giant model for everything, the trend is toward small, specialized LLMs trained for specific domains. These models are cheaper to train and deploy, and they may outperform general models on niche tasks. Using LoRA and adapter fusion, teams can compose multiple specialized adapters into a single model.
Sam Altman, CEO of OpenAI, has remarked that “the future of AI is not just bigger models but better ones—models that reason, interact multimodally and respect privacy.” This future relies on continued innovation in fine‑tuning techniques.
Emerging trends include LoRA 2.0, multimodal fine‑tuning, system‑2 reasoning, synthetic data generation, privacy‑preserving methods, and a shift toward small specialized models. Staying informed on these developments keeps your organization ahead.
Clarifai provides a platform for orchestrating AI workflows, making fine-tuning accessible, efficient, and secure.
Clarifai’s compute orchestration automatically provisions and schedules GPUs for training jobs. It adapts to the chosen fine‑tuning method (full, LoRA, QLoRA, or RLHF) and optimizes resource use. Users can run experiments concurrently and track performance metrics across runs. Integration with Hugging Face, Axolotl, and TorchTune allows seamless pipeline configuration.
For organizations with sensitive data or regulatory constraints, Local Runners enable training and inference on‑premises. They provide the same API as Clarifai’s cloud services but operate within secure environments, ensuring data never leaves corporate boundaries. This is critical for healthcare, finance, and government clients.
Beyond compute, Clarifai offers dataset management, labeling tools, model versioning, deployment, and monitoring. These services streamline the entire lifecycle from data ingestion to production inference. Teams can set up continuous integration pipelines that retrain models when new data arrives or when performance drifts.
Clarifai provides visibility into resource usage, helping teams understand the cost implications of different fine‑tuning strategies. Parameter‑efficient methods are cheaper to train; Clarifai’s dashboards quantify savings and estimate costs for upcoming jobs.
Matt Zeiler, Clarifai’s founder, explains that “our goal is to remove the friction in building and deploying AI. Fine-tuning is no longer just for big tech; with proper orchestration, any organization can harness domain-specific AI.”
Clarifai democratizes fine-tuning by offering compute orchestration, local runners, and end-to-end workflow tools. It empowers organizations to fine‑tune safely, efficiently, and cost‑effectively.
The amount of data depends on the task and method. For full fine‑tuning, tens of thousands of examples may be required, while LoRA or adapter tuning can achieve good results with a few thousand. Quality matters more than quantity—ensure data is representative and well labeled.
Fine‑tuning with curated factual data can reduce hallucinations but cannot eliminate them entirely. Complement fine‑tuning with RAG or system‑2 reasoning to provide external context and encourage self‑consistency.
Training time varies with model size, dataset size and compute resources. LoRA/QLoRA fine‑tuning can take a few hours on a single GPU, while full fine‑tuning of a 70 billion‑parameter model may require days on multi‑GPU clusters. Using Clarifai’s orchestration can parallelize jobs and reduce wall‑clock time.
For large models, GPUs are essential. Some smaller models can be fine‑tuned on CPUs, but training will be slower. Parameter‑efficient methods and 4‑bit quantization lower the GPU memory requirements. Clarifai’s infrastructure automatically matches workloads to appropriate hardware.
Using a hosted model (e.g., from Clarifai or Hugging Face) can be sufficient for many applications. Fine‑tuning becomes advantageous when you need custom behaviors, domain expertise, or compliance not offered by generic models. Evaluate existing models first and fine‑tune only if necessary.
Techniques such as LoRA, adapter fusion, mixing base data with fine‑tuning data, using smaller learning rates and shorter training durations help preserve base knowledge. Always evaluate the model on general benchmarks to ensure broad capabilities remain intact.
Popular frameworks include Hugging Face Transformers, Axolotl, TorchTune, trl (Transformers Reinforcement Learning library), and DeepSpeed. Clarifai integrates with these tools and provides an orchestration layer to manage compute and data workflows.
At the end of the FAQ, it’s worth highlighting Soumith Chintala, co‑creator of PyTorch, who recommends that newcomers start with pre‑tuned models and only experiment with fine‑tuning after understanding their baseline performance. This ensures you don’t reinvent the wheel.
Fine‑tuning requires thoughtful planning. Determine your data needs, choose the right method, allocate adequate compute and monitor safety and bias. Clarifai’s tools help streamline these steps.
Fine‑tuning remains a cornerstone of modern AI development. By adjusting the weights of pre‑trained LLMs on domain‑specific data, organizations unlock customized performance that off‑the‑shelf models can’t provide. Yet, fine‑tuning is not a panacea. It should be used judiciously alongside prompt engineering, RAG, and emerging techniques such as system‑2 reasoning. Parameter‑efficient methods like LoRA, QLoRA, Spectrum, and LoRA 2.0 lower the barrier to entry, making fine‑tuning accessible even on consumer hardware. In parallel, new challenges—safety degradation, bias, privacy, and environmental impact—demand careful evaluation and responsible practices. With tools like Clarifai’s compute orchestration and Local Runners, fine‑tuning can be efficient, secure and scalable. Keeping pace with emerging research and integrating expert insights will help your organization stay ahead in the rapidly evolving landscape of generative AI.
