Fine-tune an open-weights model when you need it to perform a stable task, follow a format, or use a style more consistently. Use retrieval-augmented generation (RAG) when it needs current, source-grounded information; use both when the application needs consistent behavior and up-to-date facts. Before training, define a measurable task and check whether prompting already meets it.
Decide whether fine-tuning is the right tool
Fine-tuning changes a model’s parameters using examples. RAG retrieves relevant information and places it in the prompt at answer time. Prompting changes the instructions and context supplied to the model without updating its parameters. These approaches address different needs:
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| Approach | Best fit | What to consider |
|---|---|---|
| Prompting | The model can do the task with clearer instructions or examples in the prompt. | Try this first if it meets your quality and consistency goals; training adds cost and operational work. |
| Fine-tuning | A stable task, output format, style, or domain language needs to be performed consistently. | You need suitable examples and a way to measure performance against the untuned model. |
| RAG | Answers depend on changing facts, a document collection, or source attribution. | Retrieved sources can inform the response, but retrieval itself does not train the model to follow a particular behavior. |
| Fine-tuning plus RAG | The application needs both a repeatable response behavior and current external facts. | Keep the responsibilities distinct: tune behavior and retrieve the information that may change. |
This distinction follows the guidance in Google Cloud’s comparison of fine-tuning and RAG. It is a decision aid, not a guarantee that one approach will outperform another for a particular application.
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Define the task and success criteria
Write down what the model receives, what a good answer must contain, and what it must avoid. Specify constraints such as an output schema, tone, allowed actions, or how to handle missing information. Then assemble a small set of representative cases for evaluation before you train. The central question is whether the tuned model does the intended task better than the untuned baseline on cases it did not train on.
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Choose measures that match the task. Exact-match or schema checks can help assess structured outputs; human review may be needed for quality judgments such as whether an answer is useful or appropriately cautious. A benchmark score by itself may not predict performance on your application’s inputs.
Check whether prompting already works
Try the base model with a clear prompt and representative examples. If that achieves the target, fine-tuning may not justify the extra training and deployment work. If the remaining errors are consistent—such as repeatedly missing a required format or applying a stable classification rule—training examples may help address them.
Choose a base model and training method
Select a model that fits the task and deployment
Check whether the model supports the task and modality, whether its license permits your intended use, and whether it fits your serving environment. Confirm that the tokenizer and chat template are supported by the training setup: examples formatted differently from the model’s expected conversation structure can undermine training. Google’s Gemma tutorial demonstrates one workflow with Gemma; it does not establish Gemma as the best choice for other tasks.
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Choose between full fine-tuning and PEFT
Full fine-tuning updates the model’s weights. Parameter-efficient fine-tuning (PEFT), including LoRA, trains adapter parameters while keeping the base weights frozen. PEFT is often a practical starting point because it limits what must be trained and managed, but performance and resource needs depend on the model, data, and setup.
QLoRA is a PEFT approach that uses a quantized base model—4-bit in the method described by its authors—while training adapters. Quantization reduces memory pressure; it does not make hardware requirements disappear or guarantee that a given model will fit a particular GPU.
Prepare examples that teach the target behavior
Build examples from the task you defined, using the inputs and outputs the model is expected to handle. Aim for accurate, representative demonstrations rather than volume for its own sake. Include meaningful variation in inputs and cover cases where the right response is to refuse, ask for clarification, or report insufficient information if those outcomes matter to your application.
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- Use examples that reflect the range and format of real inputs, including difficult or ambiguous cases.
- Keep answers consistent with the behavior and constraints you want the model to learn.
- Separate training examples from evaluation cases so the final comparison measures performance on unseen examples.
- Choose a data source—open, synthetic, human-created, or mixed—according to your quality requirements, time, and budget.
There is no universal minimum dataset size established by the cited guidance. The useful amount depends on the task and the quality and coverage of the examples. Inspect the prepared data for formatting problems and examples that contradict one another before starting a run.
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Hugging Face TRL provides SFTTrainer for supervised fine-tuning and supports PEFT configuration. Its documentation includes Python and command-line examples. Treat the LoRA settings in those examples as starting configurations, not universal hyperparameters: the right choices depend on your model, task, data, and hardware.
- Set up the environment: follow the current TRL installation instructions for the combination of packages and hardware you plan to use. TRL documents the
trl[peft]extra andbitsandbytesfor QLoRA support; check current compatibility guidance rather than assuming a package combination works on every system. - Load the model and tokenizer: use a base model that your hardware and training stack support, and ensure the tokenizer and chat template match the data format.
- Format the training data: prepare examples in the structure expected by the trainer. For conversational data, verify that turns and any required special tokens are represented correctly.
- Configure supervised fine-tuning: pass the model, dataset, and training configuration to
SFTTrainer. If using PEFT, supply a compatible PEFT configuration; for QLoRA, follow the current quantization and adapter example in the documentation. - Save the result: keep the training configuration and evaluation results with the saved model or adapters so the run can be reproduced and compared.
API details and dependencies can change. Use the current official TRL documentation for runnable code and evaluation examples rather than copying an unpinned configuration into a different environment.
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Size hardware for the actual configuration
Memory needs depend on the model and training configuration, including sequence length, batch size, quantization, and implementation. Two cited examples illustrate why a single figure is not a sizing rule: Google AI for Developers describes a Gemma 1B tutorial created for an NVIDIA T4 with 16 GB of memory, while the authors of the 2023 QLoRA paper report an experiment fine-tuning a 65B-parameter model on one 48 GB GPU. Those are different models and experiments, not minimum requirements or directly comparable hardware recommendations.
The QLoRA authors characterized their result this way: “We present QLoRA, an efficient finetuning approach that reduces memory usage enough to finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit finetuning task performance.” That statement describes the authors’ reported experiment, not a guarantee for another model, dataset, or implementation.
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Evaluate the tuned model against the baseline
Run the untuned and tuned models on the same held-out cases, using the same task instructions and comparable settings. Compare results against the success criteria you set before training, not just against a general benchmark.
- Check whether the desired behavior improved and whether any important errors became more frequent.
- Review failures by type—for example, incorrect answers, malformed output, or poor handling of ambiguous inputs—so you can decide whether the issue calls for better examples, a different prompt, retrieval, or a different model.
- Use human review when output quality is subjective, and do not rely exclusively on model-generated judgments. The QLoRA paper discusses limitations in benchmark reliability and model-based evaluation.
If the tuned model does not improve the held-out task results enough to justify its added complexity, keep the simpler approach or revise the experiment before deployment.
Deploy adapters or a merged model deliberately
With PEFT, decide whether to serve the adapter separately from the base model or merge it into the model, where the chosen workflow supports that option. The separate-adapter route keeps the base and task-specific weights distinct; merging may better fit a serving setup but changes how the resulting model is packaged. Verify runtime compatibility and the model license for the way you intend to distribute or host the result. Google’s tutorial describes both adapter deployment options.
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