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--- |
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library_name: peft |
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license: other |
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base_model: microsoft/Phi-3-mini-4k-instruct |
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tags: |
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- llama-factory |
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- lora |
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- generated_from_trainer |
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model-index: |
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- name: lora |
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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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# lora |
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This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on the flock_task5_tranning dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7054 |
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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: 5e-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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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 8 |
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- total_eval_batch_size: 2 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.05 |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 1.287 | 0.6897 | 10 | 1.4997 | |
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| 1.3264 | 1.3448 | 20 | 1.3904 | |
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| 0.9293 | 2.0 | 30 | 1.2929 | |
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| 1.2359 | 2.6897 | 40 | 1.2001 | |
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| 1.1123 | 3.3448 | 50 | 1.1125 | |
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| 0.8456 | 4.0 | 60 | 1.0130 | |
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| 0.92 | 4.6897 | 70 | 0.9277 | |
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| 0.9875 | 5.3448 | 80 | 0.8583 | |
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| 0.6225 | 6.0 | 90 | 0.8013 | |
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| 0.825 | 6.6897 | 100 | 0.7550 | |
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| 0.6775 | 7.3448 | 110 | 0.7281 | |
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| 0.6614 | 8.0 | 120 | 0.7120 | |
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| 0.6203 | 8.6897 | 130 | 0.7067 | |
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| 0.6521 | 9.3448 | 140 | 0.7054 | |
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### Framework versions |
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- PEFT 0.12.0 |
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- Transformers 4.48.3 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
