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--- |
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license: mit |
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library_name: peft |
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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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- dolly |
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- ipex |
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- max series gpu |
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base_model: microsoft/phi-1_5 |
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datasets: |
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- generator |
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model-index: |
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- name: phi-1_5-lora-tuned-sft-dolly_hitesh |
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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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# phi-1_5-lora-tuned-sft-dolly_hitesh |
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This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the generator dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.3164 |
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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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## Hardware |
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Trained model on Intel Max 1550 GPU |
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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: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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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: linear |
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- lr_scheduler_warmup_ratio: 0.05 |
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- training_steps: 1480 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-------:|:----:|:---------------:| |
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| 2.8614 | 1.6129 | 100 | 2.6779 | |
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| 2.6089 | 3.2258 | 200 | 2.5131 | |
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| 2.5117 | 4.8387 | 300 | 2.4545 | |
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| 2.4636 | 6.4516 | 400 | 2.4229 | |
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| 2.4367 | 8.0645 | 500 | 2.3990 | |
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| 2.4091 | 9.6774 | 600 | 2.3761 | |
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| 2.389 | 11.2903 | 700 | 2.3553 | |
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| 2.3639 | 12.9032 | 800 | 2.3394 | |
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| 2.3541 | 14.5161 | 900 | 2.3299 | |
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| 2.3418 | 16.1290 | 1000 | 2.3241 | |
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| 2.3395 | 17.7419 | 1100 | 2.3209 | |
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| 2.3319 | 19.3548 | 1200 | 2.3186 | |
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| 2.3363 | 20.9677 | 1300 | 2.3171 | |
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| 2.3327 | 22.5806 | 1400 | 2.3164 | |
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### Framework versions |
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- PEFT 0.11.1 |
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- Transformers 4.41.2 |
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- Pytorch 2.1.0.post0+cxx11.abi |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
