| Quantization made by Richard Erkhov. | |
| [Github](https://github.com/RichardErkhov) | |
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| opt-history-v2 - AWQ | |
| - Model creator: https://huggingface.co/ambrosfitz/ | |
| - Original model: https://huggingface.co/ambrosfitz/opt-history-v2/ | |
| Original model description: | |
| --- | |
| license: other | |
| base_model: facebook/opt-350m | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: opt-history-gen2 | |
| results: [] | |
| datasets: | |
| - ambrosfitz/just_history | |
| - ambrosfitz/synth_history_sentences | |
| - ambrosfitz/ps_history_txt | |
| - ambrosfitz/ah_analysis_multihistory | |
| language: | |
| - en | |
| pipeline_tag: text-generation | |
| --- | |
| <!-- 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. --> | |
| # opt-history-gen2 | |
| This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 2.8025 | |
| ## 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-05 | |
| - train_batch_size: 4 | |
| - eval_batch_size: 4 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 8 | |
| - total_train_batch_size: 32 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 500 | |
| - num_epochs: 6 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:------:|:----:|:---------------:| | |
| | 3.1245 | 0.1693 | 100 | 3.0013 | | |
| | 2.9473 | 0.3386 | 200 | 2.9180 | | |
| | 2.9014 | 0.5078 | 300 | 2.8787 | | |
| | 2.873 | 0.6771 | 400 | 2.8646 | | |
| | 2.8631 | 0.8464 | 500 | 2.8631 | | |
| | 2.8366 | 1.0157 | 600 | 2.8451 | | |
| | 2.64 | 1.1849 | 700 | 2.8380 | | |
| | 2.6295 | 1.3542 | 800 | 2.8202 | | |
| | 2.6272 | 1.5235 | 900 | 2.7987 | | |
| | 2.6327 | 1.6928 | 1000 | 2.7971 | | |
| | 2.626 | 1.8620 | 1100 | 2.7739 | | |
| | 2.5237 | 2.0313 | 1200 | 2.7829 | | |
| | 2.3242 | 2.2006 | 1300 | 2.7812 | | |
| | 2.319 | 2.3699 | 1400 | 2.7727 | | |
| | 2.3314 | 2.5391 | 1500 | 2.7668 | | |
| | 2.3579 | 2.7084 | 1600 | 2.7561 | | |
| | 2.307 | 2.8777 | 1700 | 2.7586 | | |
| | 2.2612 | 3.0470 | 1800 | 2.7795 | | |
| | 2.056 | 3.2163 | 1900 | 2.7801 | | |
| | 2.0802 | 3.3855 | 2000 | 2.7670 | | |
| | 2.1104 | 3.5548 | 2100 | 2.7708 | | |
| | 2.1115 | 3.7241 | 2200 | 2.7629 | | |
| | 2.0828 | 3.8934 | 2300 | 2.7606 | | |
| | 1.996 | 4.0626 | 2400 | 2.7849 | | |
| | 1.8701 | 4.2319 | 2500 | 2.7938 | | |
| | 1.92 | 4.4012 | 2600 | 2.7928 | | |
| | 1.8844 | 4.5705 | 2700 | 2.7846 | | |
| | 1.9058 | 4.7397 | 2800 | 2.7840 | | |
| | 1.901 | 4.9090 | 2900 | 2.7821 | | |
| | 1.8418 | 5.0783 | 3000 | 2.8017 | | |
| | 1.757 | 5.2476 | 3100 | 2.8055 | | |
| | 1.7503 | 5.4168 | 3200 | 2.8095 | | |
| | 1.7606 | 5.5861 | 3300 | 2.8065 | | |
| | 1.7316 | 5.7554 | 3400 | 2.8043 | | |
| | 1.7632 | 5.9247 | 3500 | 2.8025 | | |
| ### Framework versions | |
| - Transformers 4.42.4 | |
| - Pytorch 2.3.1+cu121 | |
| - Datasets 2.21.0 | |
| - Tokenizers 0.19.1 | |