license: llama3
library_name: peft
language:
- en
tags:
- trl
- sft
- unsloth
- dna
base_model: unsloth/llama-3-8b-bnb-4bit
model-index:
- name: llama3-biotoken3pretrain-kaniwa
results: []
llama3-biotoken3pretrain-kaniwa
This is a LoRA adapter.
The base model is Llama 3 quantized by Unsloth: unsloth/llama-3-8b-bnb-4bit
The tokenizer has added "biotokens" ∎A, ∎C, ∎G, and ∎T.
The dataset was ~20% of BYU's 2019 kaniwa (Chenopodium pallidicaule) genome, from https://genomevolution.org/coge/GenomeInfo.pl?gid=53872
The adapter was finetuned for several hours on an A100 GPU. The data was split into ~6k nucleotide snippets with an Alpaca like message format.
Training Notebook (before copying over to Lambda): https://colab.research.google.com/drive/1IrRBC2LKlU7_7zjzmmzslT0uDOacwyfO?usp=sharing
Sample message:
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
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained("monsoon-nlp/llama3-biotoken3pretrain-kaniwa", load_in_4bit=True).to("cuda")
tokenizer = AutoTokenizer.from_pretrained("monsoon-nlp/llama3-biotoken3pretrain-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
from transformers import AutoTokenizer
from trl import SFTTrainer
from unsloth import FastLanguageModel
model, _ = FastLanguageModel.from_pretrained(
model_name = "monsoon-nlp/llama3-biotoken3pretrain-kaniwa",
max_seq_length = 6_500, # 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-biotoken3pretrain-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.
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