Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) tinyllama-1.1b-mt-dpo-full_LR5e-8_BS16_rmsprop_2epochs - AWQ - Model creator: https://huggingface.co/martimfasantos/ - Original model: https://huggingface.co/martimfasantos/tinyllama-1.1b-mt-dpo-full_LR5e-8_BS16_rmsprop_2epochs/ Original model description: --- license: apache-2.0 base_model: martimfasantos/tinyllama-1.1b-mt-sft-full tags: - alignment-handbook - trl - dpo - generated_from_trainer - trl - dpo - generated_from_trainer datasets: - haoranxu/ALMA-R-Preference model-index: - name: tinyllama-1.1b-mt-dpo-full_LR5e-8_BS16_rmsprop_2epochs results: [] --- # tinyllama-1.1b-mt-dpo-full_LR5e-8_BS16_rmsprop_2epochs This model is a fine-tuned version of [martimfasantos/tinyllama-1.1b-mt-sft-full](https://huggingface.co/martimfasantos/tinyllama-1.1b-mt-sft-full) on the haoranxu/ALMA-R-Preference 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-08 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.41.2 - Pytorch 2.1.2 - Datasets 2.20.0 - Tokenizers 0.19.1