metadata

license: apache-2.0
base_model: microsoft/phi-2
language:
  - en
pipeline_tag: text-generation
tags:
  - alignment-handbook
  - generated_from_trainer

outputs

This model is a fine-tuned version of microsoft/phi-2 using SPIN on ultrachat_200k dataset.

What's new

I think SPIN not only can use on a SFT model, but also it can use on a pretrained model. Therefore, I use SPIN on a pretrained model microsoft/phi-2. And I get a higher score better than origin pretrained model. You can check the open llm leaderboard.

I Think the best paradigm for training a conversational Large Language Model (LLM): pretrain -> dpo(spin) -> sft -> dpo(spin)

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-07
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 8
total_train_batch_size: 64
total_eval_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1

Framework versions

Transformers 4.37.0
Pytorch 2.1.2+cu121
Datasets 2.14.6
Tokenizers 0.15.2