♻️Continued Pretraining

AKA as Continued Finetuning. Unsloth allows you to continually pretrain so a model can learn a new language.

The text completion notebook is for continued pretraining/raw text. The continued pretraining notebook is for learning another language.

You can read more about continued pretraining and our release in our blog post.

What is Continued Pretraining?

Continued or continual pretraining (CPT) is necessary to “steer” the language model to understand new domains of knowledge, or out of distribution domains. Base models like Llama-3 8b or Mistral 7b are first pretrained on gigantic datasets of trillions of tokens (Llama-3 for e.g. is 15 trillion).

But sometimes these models have not been well trained on other languages, or text specific domains, like law, medicine or other areas. So continued pretraining (CPT) is necessary to make the language model learn new tokens or datasets.

Advanced Features:

Loading LoRA adapters for continued finetuning

If you saved a LoRA adapter through Unsloth, you can also continue training using your LoRA weights. The optimizer state will be reset as well. To load even optimizer states to continue finetuning, see the next section.

from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "LORA_MODEL_NAME",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)
trainer = Trainer(...)
trainer.train()

Continued Pretraining & Finetuning the lm_head and embed_tokens matrices

Add lm_head and embed_tokens. For Colab, sometimes you will go out of memory for Llama-3 8b. If so, just add lm_head.

model = FastLanguageModel.get_peft_model(
    model,
    r = 16,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",
                      "lm_head", "embed_tokens",],
    lora_alpha = 16,
)

Then use 2 different learning rates - a 2-10x smaller one for the lm_head or embed_tokens like so:

from unsloth import UnslothTrainer, UnslothTrainingArguments

trainer = UnslothTrainer(
    ....
    args = UnslothTrainingArguments(
        ....
        learning_rate = 5e-5,
        embedding_learning_rate = 5e-6, # 2-10x smaller than learning_rate
    ),
)