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--- |
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base_model: ondevicellm/tinyllama_mole_v1 |
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tags: |
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- alignment-handbook |
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- trl |
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- sft |
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- generated_from_trainer |
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- trl |
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- sft |
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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: tinyllama_mole_sftv2_ultrachat_ep3 |
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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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# tinyllama_mole_sftv2_ultrachat_ep3 |
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This model is a fine-tuned version of [ondevicellm/tinyllama_mole_v1](https://huggingface.co/ondevicellm/tinyllama_mole_v1) on the HuggingFaceH4/ultrachat_200k dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.7340 |
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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: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 128 |
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- total_eval_batch_size: 32 |
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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: 120 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 2.7643 | 0.09 | 100 | 2.7492 | |
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| 2.7293 | 0.18 | 200 | 2.7330 | |
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| 2.6973 | 0.26 | 300 | 2.6920 | |
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| 2.612 | 0.35 | 400 | 2.6290 | |
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| 2.5257 | 0.44 | 500 | 2.5470 | |
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| 2.4656 | 0.53 | 600 | 2.4527 | |
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| 2.3607 | 0.61 | 700 | 2.3681 | |
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| 2.2885 | 0.7 | 800 | 2.2988 | |
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| 2.2384 | 0.79 | 900 | 2.2397 | |
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| 2.1585 | 0.88 | 1000 | 2.1877 | |
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| 2.1526 | 0.96 | 1100 | 2.1409 | |
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| 2.0845 | 1.05 | 1200 | 2.0986 | |
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| 2.049 | 1.14 | 1300 | 2.0603 | |
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| 2.0243 | 1.23 | 1400 | 2.0257 | |
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| 1.9899 | 1.31 | 1500 | 1.9950 | |
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| 1.9706 | 1.4 | 1600 | 1.9675 | |
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| 1.9414 | 1.49 | 1700 | 1.9429 | |
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| 1.8952 | 1.58 | 1800 | 1.9208 | |
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| 1.9038 | 1.66 | 1900 | 1.9013 | |
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| 1.8942 | 1.75 | 2000 | 1.8839 | |
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| 1.8652 | 1.84 | 2100 | 1.8679 | |
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| 1.823 | 1.93 | 2200 | 1.8531 | |
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| 1.8394 | 2.01 | 2300 | 1.8394 | |
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| 1.8347 | 2.1 | 2400 | 1.8268 | |
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| 1.8137 | 2.19 | 2500 | 1.8148 | |
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| 1.799 | 2.28 | 2600 | 1.8037 | |
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| 1.7774 | 2.37 | 2700 | 1.7931 | |
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| 1.771 | 2.45 | 2800 | 1.7832 | |
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| 1.7761 | 2.54 | 2900 | 1.7739 | |
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| 1.7458 | 2.63 | 3000 | 1.7652 | |
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| 1.7683 | 2.72 | 3100 | 1.7570 | |
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| 1.7389 | 2.8 | 3200 | 1.7490 | |
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| 1.7321 | 2.89 | 3300 | 1.7414 | |
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| 1.7418 | 2.98 | 3400 | 1.7340 | |
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### Framework versions |
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- Transformers 4.37.0 |
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- Pytorch 2.1.2+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.15.0 |
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