Instructions to use alterf/phi-2-role-play with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use alterf/phi-2-role-play with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2") model = PeftModel.from_pretrained(base_model, "alterf/phi-2-role-play") - Notebooks
- Google Colab
- Kaggle
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Download README.md from alterf/phi-2-role-play: direct link, hf CLI and curl.
- Browser
- Download file 1.08 kB
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https://huggingface.co/alterf/phi-2-role-play/resolve/4fcd7c0fd74cade1dbd4e5ed72774ebfed34ea75/README.md
- Command line
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hf download hf://alterf/phi-2-role-play@4fcd7c0fd74cade1dbd4e5ed72774ebfed34ea75/README.md
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curl -L -o README.md https://huggingface.co/alterf/phi-2-role-play/resolve/4fcd7c0fd74cade1dbd4e5ed72774ebfed34ea75/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-role-play
results: []
phi-2-role-play
This model is a fine-tuned version of microsoft/phi-2 on the None 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: 0.002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
Framework versions
- PEFT 0.12.0
- Transformers 4.43.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1