Embeddings and NLG related to biology / amino acid sequences • 10 items • Updated • 1
tinyllama-mixpretrain-uniprottune
This is an adapter of the 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
- Downloads last month
- -
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support
