Create README.md

README.md

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+---
+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