README.md

---
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
```

This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/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