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--- |
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license: llama3 |
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base_model: meta-llama/Meta-Llama-3-8B-Instruct |
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tags: |
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- trl |
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- sft |
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- generated_from_trainer |
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model-index: |
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- name: MedQA_L3_1000steps_1e5rate_SFT |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# MedQA_L3_1000steps_1e5rate_SFT |
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3681 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 4 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 100 |
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- training_steps: 1000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 0.4577 | 0.0489 | 50 | 0.5024 | |
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| 0.4969 | 0.0977 | 100 | 0.4876 | |
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| 0.4689 | 0.1466 | 150 | 0.4380 | |
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| 0.4891 | 0.1954 | 200 | 0.4313 | |
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| 0.424 | 0.2443 | 250 | 0.4275 | |
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| 0.4408 | 0.2931 | 300 | 0.4208 | |
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| 0.4124 | 0.3420 | 350 | 0.4160 | |
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| 0.4012 | 0.3908 | 400 | 0.4113 | |
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| 0.4305 | 0.4397 | 450 | 0.4285 | |
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| 0.4031 | 0.4885 | 500 | 0.3974 | |
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| 0.3863 | 0.5374 | 550 | 0.3916 | |
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| 0.3981 | 0.5862 | 600 | 0.3861 | |
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| 0.3705 | 0.6351 | 650 | 0.3810 | |
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| 0.3591 | 0.6839 | 700 | 0.3760 | |
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| 0.3642 | 0.7328 | 750 | 0.3722 | |
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| 0.3712 | 0.7816 | 800 | 0.3699 | |
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| 0.3893 | 0.8305 | 850 | 0.3686 | |
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| 0.3512 | 0.8793 | 900 | 0.3682 | |
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| 0.3546 | 0.9282 | 950 | 0.3681 | |
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| 0.3736 | 0.9770 | 1000 | 0.3681 | |
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### Framework versions |
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- Transformers 4.41.0 |
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- Pytorch 2.0.0+cu117 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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