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--- |
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license: other |
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base_model: microsoft/phi-2 |
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tags: |
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- alignment-handbook |
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- generated_from_trainer |
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datasets: |
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- HuggingFaceH4/ultrachat_200k |
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model-index: |
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- name: phi-sft-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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# phi-sft-lora |
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This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the HuggingFaceH4/ultrachat_200k dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2210 |
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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: 2e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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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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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:------:|:---------------:| |
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| 1.1971 | 0.05 | 10000 | 1.2409 | |
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| 1.1911 | 0.1 | 20000 | 1.2349 | |
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| 1.4103 | 0.14 | 30000 | 1.2317 | |
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| 1.192 | 0.19 | 40000 | 1.2295 | |
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| 1.4831 | 0.24 | 50000 | 1.2275 | |
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| 1.2857 | 0.29 | 60000 | 1.2266 | |
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| 1.014 | 0.34 | 70000 | 1.2256 | |
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| 1.2777 | 0.38 | 80000 | 1.2251 | |
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| 0.9019 | 0.43 | 90000 | 1.2241 | |
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| 1.1926 | 0.48 | 100000 | 1.2235 | |
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| 1.2298 | 0.53 | 110000 | 1.2233 | |
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| 1.1102 | 0.58 | 120000 | 1.2228 | |
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| 1.3166 | 0.63 | 130000 | 1.2219 | |
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| 1.1452 | 0.67 | 140000 | 1.2217 | |
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| 1.308 | 0.72 | 150000 | 1.2217 | |
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| 0.9096 | 0.77 | 160000 | 1.2215 | |
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| 1.2817 | 0.82 | 170000 | 1.2211 | |
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| 1.2904 | 0.87 | 180000 | 1.2211 | |
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| 0.9066 | 0.91 | 190000 | 1.2210 | |
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| 1.1807 | 0.96 | 200000 | 1.2210 | |
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### Framework versions |
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- Transformers 4.35.0 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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