Write information about the nucleotide sequence.

### Sequence:
∎G∎C∎C∎T∎A∎T∎A∎G∎T∎G∎T∎G∎T∎A∎G...

### Annotation:
Information about location in the kaniwa chromosome: >lcl|Cp5

Usage

Inference with DNA sequence

python

from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer

model = AutoPeftModelForCausalLM.from_pretrained("monsoon-nlp/llama3-biotokenpretrain-kaniwa", load_in_4bit=True).to("cuda")
tokenizer = AutoTokenizer.from_pretrained("monsoon-nlp/llama3-biotokenpretrain-kaniwa")
tokenizer.pad_token = tokenizer.eos_token # pad fix

qed = "∎" # from math symbols, used in pretraining
sequence = "".join([(qed + nt.upper()) for nt in "GCCTATAGTGTGTAGCTAATGAGCCTAGGTTATCGACCCTAATCT"])

inputs = tokenizer(f"{prefix}{sequence}{annotation}", return_tensors="pt")
outputs = model.generate(input_ids=inputs["input_ids"].to("cuda"), max_new_tokens=50)
sample = tokenizer.batch_decode(outputs, skip_special_tokens=False)[0]

LoRA finetuning on a new task

python

from transformers import AutoTokenizer
from trl import SFTTrainer
from unsloth import FastLanguageModel

model, _ = FastLanguageModel.from_pretrained(
    model_name = "monsoon-nlp/llama3-biotokenpretrain-kaniwa",
    max_seq_length = 7_000, # max 6,000 bp for AgroNT tasks
    dtype = None,
    load_in_4bit = True,
    resize_model_vocab=128260, # includes biotokens
)
tokenizer = AutoTokenizer.from_pretrained("monsoon-nlp/llama3-biotokenpretrain-kaniwa")
tokenizer.pad_token = tokenizer.eos_token # pad fix

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
...
)

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 1
eval_batch_size: 8
seed: 3407
gradient_accumulation_steps: 4
total_train_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 5
training_steps: 280

Framework versions

PEFT 0.10.0
Transformers 4.40.2
Pytorch 2.2.1+cu121
Datasets 2.19.1
Tokenizers 0.19.1

Genome Citation

Mangelson H, et al. The genome of Chenopodium pallidicaule: an emerging Andean super grain. Appl. Plant Sci. 2019;7:e11300. doi: 10.1002/aps3.11300