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--- |
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base_model: meta-llama/Meta-Llama-3.1-8B-Instruct |
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library_name: peft |
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license: llama3.1 |
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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: Llama-31-8B_task-1_60-samples_config-2_auto |
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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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# Llama-31-8B_task-1_60-samples_config-2_auto |
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.5471 |
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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: 0.0001 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 16 |
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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_ratio: 0.1 |
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- num_epochs: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-------:|:----:|:---------------:| |
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| 1.1077 | 0.6957 | 2 | 1.2225 | |
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| 1.1089 | 1.7391 | 5 | 1.1334 | |
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| 0.954 | 2.7826 | 8 | 1.0066 | |
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| 0.9473 | 3.8261 | 11 | 0.9689 | |
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| 0.7929 | 4.8696 | 14 | 0.9281 | |
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| 0.7943 | 5.9130 | 17 | 0.9352 | |
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| 0.6696 | 6.9565 | 20 | 0.9296 | |
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| 0.5976 | 8.0 | 23 | 0.9200 | |
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| 0.5767 | 8.6957 | 25 | 0.9291 | |
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| 0.4423 | 9.7391 | 28 | 0.9620 | |
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| 0.4059 | 10.7826 | 31 | 1.0431 | |
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| 0.2734 | 11.8261 | 34 | 1.1927 | |
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| 0.2387 | 12.8696 | 37 | 1.3013 | |
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| 0.1347 | 13.9130 | 40 | 1.4096 | |
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| 0.0897 | 14.9565 | 43 | 1.5471 | |
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### Framework versions |
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- PEFT 0.12.0 |
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- Transformers 4.44.0 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
