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--- |
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license: other |
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base_model: microsoft/phi-2 |
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tags: |
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- generated_from_trainer |
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- medical |
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- peft |
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- 'lora ' |
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model-index: |
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- name: Thealth-phi-2-tunned-9_medalpaca_medical_meadow |
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results: [] |
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datasets: |
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- medalpaca/medical_meadow_mediqa |
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- medalpaca/medical_meadow_mmmlu |
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- medalpaca/medical_meadow_medical_flashcards |
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- medalpaca/medical_meadow_wikidoc_patient_information |
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- medalpaca/medical_meadow_wikidoc |
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- medalpaca/medical_meadow_pubmed_causal |
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- medalpaca/medical_meadow_medqa |
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- medalpaca/medical_meadow_health_advice |
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- medalpaca/medical_meadow_cord19 |
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pipeline_tag: conversational |
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--- |
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 |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Thealth-phi-2-tunned-9_medalpaca_medical_meadow |
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This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 6.6588 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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Training is done one 9 medalpaca/medical_meadow datasets combined and splited to 90% train and 10% Evaluation |
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| Dataset | |
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|:-----------------------------------------------------:| |
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| medalpaca/medical_meadow_mediqa | |
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| medalpaca/medical_meadow_mmmlu | |
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| medalpaca/medical_meadow_medical_flashcards | |
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| medalpaca/medical_meadow_wikidoc_patient_information | |
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| medalpaca/medical_meadow_wikidoc | |
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| medalpaca/medical_meadow_pubmed_causal | |
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| medalpaca/medical_meadow_medqa | |
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| medalpaca/medical_meadow_health_advice | |
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| medalpaca/medical_meadow_cord19 | |
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## Training procedure |
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Used different tokenizer [stanford-crfm/BioMedLM](https://huggingface.co/stanford-crfm/BioMedLM) |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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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: cosine |
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- training_steps: 1000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 6.8245 | 0.0 | 500 | 6.7654 | |
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| 6.7944 | 0.0 | 1000 | 6.6588 | |
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### Usage |
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```python |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("stanford-crfm/BioMedLM", trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained("TachyHealthResearch/Thealth-phi-2-tunned-9_medalpaca_medical_meadow", trust_remote_code=True, torch_dtype=torch.float32) |
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``` |
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```python |
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inputs = tokenizer( |
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""" |
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question: ****** ? answer: |
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""", |
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return_tensors="pt", |
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return_attention_mask=False) |
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``` |
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```python |
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outputs = model.generate(**inputs, max_length=512) |
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text = tokenizer.batch_decode(outputs)[0] |
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print(text) |
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``` |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
