--- base_model: JunxiongWang/llama3_0_50_mamba2_sft tags: - alignment-handbook - generated_from_trainer datasets: - HuggingFaceH4/ultrafeedback_binarized - HuggingFaceH4/orca_dpo_pairs - JunxiongWang/llama3-ultrafeedback-armorm model-index: - name: JunxiongWang/Mamba2InLlama_0_50 results: [] --- [Visualize in Weights & Biases](https://wandb.ai/junxiong12/huggingface/runs/ovbim1mz) # JunxiongWang/Mamba2InLlama_0_50 This model is a fine-tuned version of [JunxiongWang/llama3_0_50_mamba2_sft] on the HuggingFaceH4/ultrafeedback_binarized, the HuggingFaceH4/orca_dpo_pairs and the JunxiongWang/llama3-ultrafeedback-armorm datasets. It achieves the following results on the evaluation set: - Loss: 0.4340 - Rewards/chosen: -2.3310 - Rewards/rejected: -4.2908 - Rewards/accuracies: 0.8214 - Rewards/margins: 1.9598 - Logps/rejected: -707.1605 - Logps/chosen: -505.8361 - Logits/rejected: 1.0544 - Logits/chosen: 1.1061 ## 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-07 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 32 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.4605 | 0.4798 | 2000 | 0.4675 | -1.7509 | -3.3086 | 0.8107 | 1.5578 | -608.9371 | -447.8168 | 0.6185 | 0.6654 | | 0.4475 | 0.9597 | 4000 | 0.4340 | -2.3310 | -4.2908 | 0.8214 | 1.9598 | -707.1605 | -505.8361 | 1.0544 | 1.1061 | ### Framework versions - Transformers 4.43.1 - Pytorch 2.1.1+cu118 - Datasets 2.20.0 - Tokenizers 0.19.1