Fine-Tuning Falcon 2 11B: Why TII Recommends It

Why fine-tuning is the expected path

A pretrained model predicts the next token well; it does not follow instructions. Instruction following is a separate training stage that TII applied to the backbone used for the VLM but did not publish as a text instruct checkpoint. If you need conversation from the text model, that stage is yours to run.

What to plan for

A dataset of instruction and response pairs in your target format, not just your domain text.
Parameter-efficient methods such as LoRA to fit training on a single GPU.
An evaluation set built before training starts, so you can tell improvement from drift.
A licence check on your training data as well as on the model.

A cheaper first move

Before fine-tuning, try the VLM checkpoint as a text model. It was built from a chat-finetuned Falcon 2 11B, so it follows instructions better out of the box, and that may be enough without a training run at all.

What fine-tuning does not fix

Fine-tuning does not extend the 8,192 token context window, add languages the model never saw, or turn an 11B model into a larger one. If your problem is long documents or an unsupported language, training on your own data is the wrong lever.

Sources

falcon2.lol is an independent third-party site. It is not affiliated with, endorsed by, sponsored by or operated by the Technology Innovation Institute, and it does not speak for TII. Every number on this page is attributed to the source listed above.