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# Trainable Tokens

The Trainable Tokens method provides a way to target specific token embeddings for fine-tuning without resorting to
training the full embedding matrix or using an adapter on the embedding matrix. It is based on the initial implementation from
[here](https://github.com/huggingface/peft/pull/1541).

The method only targets specific tokens and selectively trains the token indices you specify. Consequently the
required RAM will be lower and disk memory is also significantly lower than storing the full fine-tuned embedding matrix.

Some preliminary benchmarks acquired with [this script](https://github.com/huggingface/peft/blob/main/scripts/train_memory.py)
suggest that for `gemma-2-2b` (which has a rather large embedding matrix) you can save ~4 GiB VRAM with Trainable Tokens
over fully fine-tuning the embedding matrix. While LoRA will use comparable amounts of VRAM it might also target
tokens you don't want to be changed. Note that these are just indications and varying embedding matrix sizes might skew
these numbers a bit.

Note that this method does not add tokens for you, you have to add tokens to the tokenizer yourself and resize the
embedding matrix of the model accordingly. This method will only re-train the embeddings for the tokens you specify.
This method can also be used in conjunction with LoRA layers! See [the LoRA developer guide](../developer_guides/lora#efficiently-train-tokens-alongside-lora).

## TrainableTokensConfig

[[autodoc]] tuners.trainable_tokens.config.TrainableTokensConfig

## TrainableTokensModel

[[autodoc]] tuners.trainable_tokens.model.TrainableTokensModel

