# Train using supervised examples Requirements ``` wandb evaluate datasets transformers torch ``` Start training reward model ```bash python trainer.py --configs defaults galactica-125 ``` ## Dataset For now we only support webgpt and summary dataset from OpenAI. Once open-asisstant dataset are available it will be added here. ## Model Normally you should be able to add new models in configs/config.yml ``` your-model-name: learning_rate: 2e-6 model_name: weight_decay: 0.01 max_length: 812 warmup_steps: 600 gradient_checkpointing: false gradient_accumulation_steps: 5 per_device_train_batch_size: 4 per_device_eval_batch_size: 4 ``` ``` python trainer.py --configs defaults your-model-name ``` However, if the model of your choice doesn't have pad_token, eos_token, sep_token, you have to update utils.py `get_tokenizer` to use the right token. ## Deepspeed support You can edit the configs/zero_config.json and use any stage you wish. The current config uses zero-stage 3. For more details on how to setup the config checkout [this page](https://www.deepspeed.ai/tutorials/zero/) Once you are satisfy with your deepzero config, you can add --deepspeed flag at the end to trigger deepspeed ``` python trainer.py --configs defaults your-model-name --deepspeed ``` ## Dataset choices To specify which translation pair for [WMT](https://huggingface.co/datasets/wmt19) and [TED Talk](https://huggingface.co/datasets/ted_talks_iwslt) translation simply add the supported language pair at the postfix ``` datasets: - wmt2019_zh-en - wmt2019_ru-en - wmt2019_de-en - ted_trans_nl-en - ted_trans_de-ja ``` Currently only these languages are supported via prompt translation: ``` ar,de,fr,en,it,nl,tr,ru,ms,ko,ja,zh ``` ## Results Experimental results in wandb [here](https://wandb.ai/sanagnos/supervised-finetuning?workspace=user-sanagnos). ## TODOS - decide on a model - Merge utils etc with reward model - Casual Modelling for GPT-JT does not leverage the bidirectional mask for the prompt? (https://huggingface.co/togethercomputer/GPT-JT-6B-v1)