Llama3-2-3B-IT-Byte 🔢

Llama3.2-3B transferred to byte-level tokenization via cross-tokenizer distillation.

🚧This model is intended as a proof-of-concept that we can quickly & effectively transfer pretrained (subword-based) models to the byte-level. It is not optimized for production use (in particular, it is not optimized for speed)!🚧

Benchmarks

Llama3-2-3B-IT-Byte performs competitively although it has been trained only on 1.3B bytes (328M subword tokens total).

	MMLU	BoolQ	PiQA	IFEval	ARC-C	Avg.
EvaByte-6.5B-SFT	49.5	79.5*	74.1*	60.2	64.6*	65.6
Llama3.2-3B-Instruct (original)	62.4	78.8	76.9	76.6	43.9	67.7
Gemma2-2B-IT (original)	56.9	83.8	79.6	62.5	50.4	66.6
Llama3-2-3B-IT-Byte (this model)	57.0	76.6	73.6	58.8	39.8	61.2
Gemma2-2B-IT-Byte	51.0	80.5	71.5	51.9	38.2	58.6

*Numbers from EvaByte-6.5B (Base) since they are not reported for the SFT model.

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("benjamin/Llama3-2-3B-IT-Byte")
print("Vocab Size:", len(tokenizer))  # 256 bytes + some special tokens

device = "cuda"
model = AutoModelForCausalLM.from_pretrained(
    "benjamin/Llama3-2-3B-IT-Byte", trust_remote_code=True
)
model = model.to(device)

tokens = tokenizer.apply_chat_template(
    [{"role": "user", "content": "Hello, how are you doing?"}], return_tensors="pt"
)
eot_id = tokenizer.convert_tokens_to_ids("<|eot_id|>")
out = model.generate(tokens.to(model.device), eos_token_id=eot_id)
print(tokenizer.decode(out[0]))

Training

This model has been trained using tokenkit with the following command:

python3 scripts/cross_tokenizer_distill.py \
    --config=configs/cross_tokenizer_distill.yaml \
    --overrides \
    losses=[sft,alm_unconstrained,alm_latents] \
    multitask_aggregation_fn=approx_gradmag_preserve_mag \
    alm_mode=merge_by_space_prob+append_space \
    tokenizer_pair_bias_threshold=0.1 \
    max_student_length=2048 \
    steps=20000 \
    eval_interval=20000 \
    save_interval=20000 \
    optimizer.learning_rate=3.e-5 \
    optimizer.weight_decay=0.0 \
    optimizer.max_grad_norm=null \
    optimizer.grad_acc_steps=1 \
    train_model_mode=full \
    expand_input_ids=true \
    output_embeddings_mode=untie \
    eval.tasks=[arc_easy,arc_challenge,piqa,boolq,arithmetic,mmlu,ifeval,agieval_en,agieval_cn] \
    data.batch_size=32 \
    student.pretrained_model_name_or_path=benjamin/Llama-3.2-3B-Instruct-flax \
    student.tokenizer_name=meta-llama/Llama-3.2-3B-Instruct:source=Llama3 \
    target_tokenizer_name=meta-llama/Llama-3.2-3B-Instruct:source=Llama3:target=Llama3:conversion=byte \
    n_model_parallel=4 \
    n_data_parallel=4 \
    data.num_workers=16 \
    num_workers=16 \
    name=llama3_to_byte_20k

Training took ~26 hours on a TPU v4-32.

Future Work

The current version of this model is trained for 20k steps with 32*2048 bytes per batch (= 1.3B bytes ≈ 328M subword tokens total). It was unexpected that it performs as well as it does with this very short training procedure. We plan to train a new version for more steps (you can also do so yourself using tokenkit).

To preserve efficiency, we would have to add (a combination of) BLT-style hierarchical processing, attention approximations, and self-speculative decoding.

Acknowledgments

Training was enabled by Cloud TPUs from Google’s TPU Research Cloud (TRC).