| | --- |
| | library_name: transformers |
| | license: other |
| | |
| | base_model: meta-llama/Llama-3.1-8B |
| | tags: |
| | - llama-factory |
| | - full |
| | - generated_from_trainer |
| | model-index: |
| | - name: llama |
| | results: [] |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # llama |
| |
|
| | This model is a fine-tuned version of [/inspire/hdd/ws-8207e9e2-e733-4eec-a475-cfa1c36480ba/embodied-multimodality/public/yli/workspace/Model/meta-llama/Llama-3.1-8B](https://huggingface.co//inspire/hdd/ws-8207e9e2-e733-4eec-a475-cfa1c36480ba/embodied-multimodality/public/yli/workspace/Model/meta-llama/Llama-3.1-8B) on the 2wikimultihopqa_train, the hotpotqa_train and the musique_train datasets. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.1784 |
| | |
| | ## 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: 1e-05 |
| | - train_batch_size: 2 |
| | - eval_batch_size: 1 |
| | - seed: 42 |
| | - distributed_type: multi-GPU |
| | - num_devices: 8 |
| | - gradient_accumulation_steps: 4 |
| | - total_train_batch_size: 64 |
| | - total_eval_batch_size: 8 |
| | - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| | - lr_scheduler_type: cosine |
| | - lr_scheduler_warmup_ratio: 0.1 |
| | - num_epochs: 3.0 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | |
| | |:-------------:|:------:|:----:|:---------------:| |
| | | 0.2074 | 0.1921 | 500 | 0.1999 | |
| | | 0.2202 | 0.3843 | 1000 | 0.1942 | |
| | | 0.1933 | 0.5764 | 1500 | 0.1811 | |
| | | 0.1909 | 0.7686 | 2000 | 0.1720 | |
| | | 0.1637 | 0.9607 | 2500 | 0.1639 | |
| | | 0.0983 | 1.1528 | 3000 | 0.1664 | |
| | | 0.1033 | 1.3450 | 3500 | 0.1602 | |
| | | 0.1145 | 1.5371 | 4000 | 0.1557 | |
| | | 0.105 | 1.7293 | 4500 | 0.1506 | |
| | | 0.1008 | 1.9214 | 5000 | 0.1454 | |
| | | 0.031 | 2.1136 | 5500 | 0.1753 | |
| | | 0.0313 | 2.3057 | 6000 | 0.1777 | |
| | | 0.0323 | 2.4978 | 6500 | 0.1765 | |
| | | 0.0311 | 2.6900 | 7000 | 0.1781 | |
| | | 0.0277 | 2.8821 | 7500 | 0.1787 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.46.1 |
| | - Pytorch 2.4.0 |
| | - Datasets 3.1.0 |
| | - Tokenizers 0.20.3 |
| |
|
