Instructions to use NovoCode/Tiger-DPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use NovoCode/Tiger-DPO with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NeuralNovel/Tiger-7B-v0.1") model = PeftModel.from_pretrained(base_model, "NovoCode/Tiger-DPO") - Notebooks
- Google Colab
- Kaggle
metadata
license: other
library_name: peft
tags:
- llama-factory
- lora
- generated_from_trainer
base_model: NeuralNovel/Tiger-7B-v0.1
model-index:
- name: train_2024-02-16-12-09-1733333
results: []
train_2024-02-16-12-09-1733333
This model is a fine-tuned version of NeuralNovel/Tiger-7B-v0.1 on the Neural-DPO 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1.0
Training results
Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2