README.md

---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: monsoon-nlp/tinyllama-mixpretrain-quinoa-sciphi
model-index:
- name: tinyllama-mixpretrain-uniprottune
  results: []
datasets:
- monsoon-nlp/greenbeing-proteins
---

# tinyllama-mixpretrain-uniprottune

This is an adapter of the [monsoon-nlp/tinyllama-mixpretrain-quinoa-sciphi](https://huggingface.co/monsoon-nlp/tinyllama-mixpretrain-quinoa-sciphi) 
model on the GreenBeing dataset finetuning split (minus maize/corn/*Zea*, which I left for evaluation).

## Usage

```
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer

# this model
model = AutoPeftModelForCausalLM.from_pretrained("monsoon-nlp/tinyllama-mixpretrain-uniprottune").to("cuda")
# base model for the tokenizer
tokenizer = AutoTokenizer.from_pretrained("monsoon-nlp/tinyllama-mixpretrain-quinoa-sciphi")

inputs = tokenizer("<sequence> Subcellular locations:", return_tensors="pt")
outputs = model.generate(input_ids=inputs["input_ids"].to("cuda"), max_new_tokens=50)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])
```

Inference Notebook: https://colab.research.google.com/drive/1UTavcVpqWkp4C_GkkS_HxDQ0Orpw43iu?usp=sharing

It seems unreliable on the *Zea* proteins. Getting a lot of the same answers for Subcellular locations.


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 20
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 1



### Framework versions

- PEFT 0.10.0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2