license: llama3
library_name: peft
language:
- en
tags:
- trl
- sft
- unsloth
- generated_from_trainer
- dna
base_model: gradientai/Llama-3-8B-Instruct-262k
model-index:
- name: llama3-biotokenpretrain-kaniwa
results: []
llama3-biotokenpretrain-kaniwa
This is a LoRA adapter.
The base model is the longer-context LLaMA-3-8b-Instruct developed by Gradient and Crusoe: gradientai/Llama-3-8B-Instruct-262k
The tokenizer has added "biotokens" ∎A, ∎C, ∎G, and ∎T.
The dataset was 0.5% of BYU's 2019 kaniwa (Chenopodium pallidicaule) genome, from https://genomevolution.org/coge/GenomeInfo.pl?gid=53872
The adapter was finetuned for 3 hours on an L4 GPU. The data was split into ~7k nucleotide snippets with an Alpaca like message format.
Training Notebook: https://colab.research.google.com/drive/1FKA3p_jnfRHYd-hqJdYmKn8MQpxec0t5?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-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) 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-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