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Thealth-phi-2-tunned-9_medalpaca_medical_meadow
This model is a fine-tuned version of microsoft/phi-2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 6.6588
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
Training is done one 9 medalpaca/medical_meadow datasets combined and splited to 90% train and 10% Evaluation
| Dataset |
|---|
| medalpaca/medical_meadow_mediqa |
| medalpaca/medical_meadow_mmmlu |
| medalpaca/medical_meadow_medical_flashcards |
| medalpaca/medical_meadow_wikidoc_patient_information |
| medalpaca/medical_meadow_wikidoc |
| medalpaca/medical_meadow_pubmed_causal |
| medalpaca/medical_meadow_medqa |
| medalpaca/medical_meadow_health_advice |
| medalpaca/medical_meadow_cord19 |
Training procedure
Used different tokenizer stanford-crfm/BioMedLM
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 1000
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 6.8245 | 0.0 | 500 | 6.7654 |
| 6.7944 | 0.0 | 1000 | 6.6588 |
Usage
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stanford-crfm/BioMedLM", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("TachyHealthResearch/Thealth-phi-2-tunned-9_medalpaca_medical_meadow", trust_remote_code=True, torch_dtype=torch.float32)
inputs = tokenizer(
"""
question: ****** ? answer:
""",
return_tensors="pt",
return_attention_mask=False)
outputs = model.generate(**inputs, max_length=512)
text = tokenizer.batch_decode(outputs)[0]
print(text)
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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