Instructions to use enriquesaou/phi2_med with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use enriquesaou/phi2_med with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2") model = PeftModel.from_pretrained(base_model, "enriquesaou/phi2_med") - Notebooks
- Google Colab
- Kaggle
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Download README.md from enriquesaou/phi2_med: direct link, hf CLI and curl.
- Browser
- Download file 1.16 kB
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https://huggingface.co/enriquesaou/phi2_med/resolve/main/README.md
- Command line
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hf download hf://enriquesaou/phi2_med/README.md
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curl -L -o README.md https://huggingface.co/enriquesaou/phi2_med/resolve/main/README.md
1.16 kB
metadata
license: mit
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: microsoft/phi-2
model-index:
- name: phi2_med
results: []
phi2_med
This model is a fine-tuned version of microsoft/phi-2 on an unknown dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
Framework versions
- PEFT 0.10.1.dev0
- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2