Instructions to use prakhar123/phi-2-docker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use prakhar123/phi-2-docker with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2") model = PeftModel.from_pretrained(base_model, "prakhar123/phi-2-docker") - Notebooks
- Google Colab
- Kaggle
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Download README.md from prakhar123/phi-2-docker: direct link, hf CLI and curl.
- Browser
- Download file 1.08 kB
-
https://huggingface.co/prakhar123/phi-2-docker/resolve/main/README.md
- Command line
-
hf download hf://prakhar123/phi-2-docker/README.md
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curl -L -o README.md https://huggingface.co/prakhar123/phi-2-docker/resolve/main/README.md
1.08 kB
metadata
base_model: microsoft/phi-2
library_name: peft
license: mit
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: phi-2-docker
results: []
phi-2-docker
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: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 0.5
Training results
Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.0
- Datasets 3.0.0
- Tokenizers 0.19.1