---
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

```python
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

```python
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](https://github.com/unslothai/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