LLM vs VLM Fine-Tuning: Key Differences and When to Choose Each

Text-only fine-tuning trains a model to take text in and produce text out: text tokens in, text tokens out. Datasets are text-only examples — instruction-response pairs for SFT, preference pairs for alignment, raw text for continued pre-training.

LLMs fit question answering, summarisation, code generation, classification, and conversational AI. The fine-tuning process is straightforward: tokenise the text, compute the language modeling loss on the target tokens, backpropagate. The platform supports LLM fine-tuning across all methods and adapter types.

Vision-language fine-tuning trains models that process images and text together. VLMs typically carry a vision encoder converting images to feature vectors, a projection layer mapping visual features into the text model’s embedding space, and a language model processing the combined visual and text tokens.

VLM fine-tuning on the platform covers architectures including InternVL, Qwen-VL, LLaVA, DeepSeek-VL, and MiniCPM-V. Training feeds image-text pairs through the full pipeline, with gradients flowing through all components — or just the adapter layers under LoRA/QLoRA.

LLM datasets are pure text: SFT uses JSONL with instruction, input, and output fields; preference sets add chosen and rejected; continued pre-training uses raw text files.

VLM datasets add image references beside the text. Each example carries an image path or URL, an instruction or question about the image, and the expected text response; preference alignment adds chosen and rejected to the same image-question pair. Image loading, resizing, and encoding are handled automatically per the model architecture.

Upload validates both text and image references in VLM datasets:

VLM fine-tuning wants more memory than equivalent LLM fine-tuning: the vision encoder adds parameters, and image tokens extend the sequence length. One high-resolution image can produce 256 to 2048 visual tokens, significantly stretching the effective context.

For example, fine-tuning InternVL2-8B with LoRA needs roughly 24GB VRAM against about 16GB for a text-only 8B. The vision encoder’s parameters (typically 300M-600M) add to the footprint, and the longer effective sequences increase activation memory in the forward pass.

Choose LLM fine-tuning when the task is purely text-based: chatbots, code assistants, text classification, summarisation, domain-specific knowledge injection. LLMs are simpler to train, need less memory, and carry a more mature tooling ecosystem.

Choose VLM fine-tuning when the task requires understanding images: document analysis, visual question answering, OCR, image captioning, medical image interpretation — any workflow where the model must reason about visual content. The same configuration experience serves both, so the choice comes down to the data and the use case.

The wizard provides one configuration surface for LLM and VLM fine-tuning alike:

The most common misstep is choosing VLM fine-tuning when a text-only LLM would suffice. If the visual content can be adequately described in text — structured data displayed in a table but already available as CSV — a text-only LLM is simpler, faster to train, cheaper to deploy. Choose VLM only when the visual information is genuinely necessary and cannot be represented as text.

Another trap is underestimating VLM dataset requirements. VLMs need more examples than text-only models because they learn visual associations and text generation together. Teams getting good results with 2,000 text-only examples often find the same count of image-text pairs insufficient; plan at least 5,000 image-text pairs as a minimum.

Another frequent error is unfreezing the vision encoder unnecessarily. The pre-trained encoder learned reliable visual features from millions of images; for most tasks — document understanding, product analysis, chart reading — those features are already excellent and should be preserved. Unfreeze only when the images are truly unlike anything in pre-training — rare outside specialised scientific or medical imaging.

Finally, the inference cost difference gets overlooked. VLMs cost more to serve because they process image tokens and text tokens together, image tokens often adding 256 to 2048 tokens per image. Factor that into deployment planning, especially for high-throughput applications.

One unified workflow serves both LLM and VLM fine-tuning, so both approaches can be tried on the same platform without switching tools or learning new interfaces. The wizard adapts its options to the selected model type: a text-only model shows LLM-specific parameters; a VLM model adds image-related configuration.

That unified experience makes comparison experiments practical. Unsure whether the task needs visual understanding? Prepare both a text-only dataset and an image-text dataset, run LoRA fine-tuning on both model types, and compare — affordably, because billing covers only the compute each run actually consumes.

For teams working across text-only and multimodal applications, the platform holds everything in one workspace: datasets, training runs, and checkpoints organised together, with results from both LLM and VLM experiments viewable side by side.