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--- |
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license: apache-2.0 |
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library_name: peft |
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tags: |
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- generated_from_trainer |
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base_model: monsoon-nlp/tinyllama-mixpretrain-quinoa-sciphi |
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model-index: |
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- name: tinyllama-mixpretrain-uniprottune |
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results: [] |
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datasets: |
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- monsoon-nlp/greenbeing-proteins |
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--- |
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# tinyllama-mixpretrain-uniprottune |
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This is an adapter of the [monsoon-nlp/tinyllama-mixpretrain-quinoa-sciphi](https://huggingface.co/monsoon-nlp/tinyllama-mixpretrain-quinoa-sciphi) |
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model on the GreenBeing dataset finetuning split (minus maize/corn/*Zea*, which I left for evaluation). |
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## Usage |
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``` |
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from peft import AutoPeftModelForCausalLM |
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from transformers import AutoTokenizer |
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# this model |
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model = AutoPeftModelForCausalLM.from_pretrained("monsoon-nlp/tinyllama-mixpretrain-uniprottune").to("cuda") |
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# base model for the tokenizer |
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tokenizer = AutoTokenizer.from_pretrained("monsoon-nlp/tinyllama-mixpretrain-quinoa-sciphi") |
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inputs = tokenizer("<sequence> Subcellular locations:", return_tensors="pt") |
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outputs = model.generate(input_ids=inputs["input_ids"].to("cuda"), max_new_tokens=50) |
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print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]) |
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``` |
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Inference Notebook: https://colab.research.google.com/drive/1UTavcVpqWkp4C_GkkS_HxDQ0Orpw43iu?usp=sharing |
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It seems unreliable on the *Zea* proteins. Getting a lot of the same answers for Subcellular locations. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 20 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 10 |
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- num_epochs: 1 |
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### Framework versions |
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- PEFT 0.10.0 |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.15.2 |
