Instructions to use RuoxiL/style-irs-from-facebook-2.7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use RuoxiL/style-irs-from-facebook-2.7 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("facebook/opt-2.7b") model = PeftModel.from_pretrained(base_model, "RuoxiL/style-irs-from-facebook-2.7") - Notebooks
- Google Colab
- Kaggle
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Download README.md from RuoxiL/style-irs-from-facebook-2.7: direct link, hf CLI and curl.
- Browser
- Download file 1.25 kB
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https://huggingface.co/RuoxiL/style-irs-from-facebook-2.7/resolve/main/README.md
- Command line
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hf download hf://RuoxiL/style-irs-from-facebook-2.7/README.md
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curl -L -o README.md https://huggingface.co/RuoxiL/style-irs-from-facebook-2.7/resolve/main/README.md
1.25 kB
metadata
license: other
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: facebook/opt-2.7b
datasets:
- generator
model-index:
- name: style-irs-from-facebook-2.7
results: []
style-irs-from-facebook-2.7
This model is a fine-tuned version of facebook/opt-2.7b on the generator 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
Training results
Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.36.2
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.2