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--- |
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base_model: ondevicellm/tinyllama_moe_v2 |
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tags: |
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- alignment-handbook |
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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_moe_sft_ultrachat_v2_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_moe_sft_ultrachat_v2_ep3 |
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This model is a fine-tuned version of [ondevicellm/tinyllama_moe_v2](https://huggingface.co/ondevicellm/tinyllama_moe_v2) on the HuggingFaceH4/ultrachat_200k dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1289 |
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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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| 1.4892 | 0.09 | 100 | 1.4465 | |
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| 1.2729 | 0.18 | 200 | 1.2643 | |
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| 1.2286 | 0.26 | 300 | 1.2280 | |
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| 1.2007 | 0.35 | 400 | 1.2075 | |
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| 1.1688 | 0.44 | 500 | 1.1933 | |
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| 1.1872 | 0.53 | 600 | 1.1830 | |
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| 1.1732 | 0.61 | 700 | 1.1746 | |
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| 1.1596 | 0.7 | 800 | 1.1679 | |
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| 1.1546 | 0.79 | 900 | 1.1622 | |
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| 1.1366 | 0.88 | 1000 | 1.1572 | |
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| 1.1606 | 0.96 | 1100 | 1.1527 | |
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| 1.0967 | 1.05 | 1200 | 1.1505 | |
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| 1.099 | 1.14 | 1300 | 1.1480 | |
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| 1.1099 | 1.23 | 1400 | 1.1453 | |
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| 1.1015 | 1.31 | 1500 | 1.1432 | |
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| 1.104 | 1.4 | 1600 | 1.1408 | |
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| 1.0998 | 1.49 | 1700 | 1.1390 | |
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| 1.0829 | 1.58 | 1800 | 1.1369 | |
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| 1.1052 | 1.66 | 1900 | 1.1353 | |
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| 1.1082 | 1.75 | 2000 | 1.1336 | |
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| 1.0948 | 1.84 | 2100 | 1.1320 | |
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| 1.0682 | 1.93 | 2200 | 1.1308 | |
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| 1.0688 | 2.01 | 2300 | 1.1318 | |
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| 1.0754 | 2.1 | 2400 | 1.1317 | |
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| 1.0646 | 2.19 | 2500 | 1.1311 | |
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| 1.058 | 2.28 | 2600 | 1.1305 | |
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| 1.0553 | 2.37 | 2700 | 1.1301 | |
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| 1.0607 | 2.45 | 2800 | 1.1298 | |
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| 1.0669 | 2.54 | 2900 | 1.1294 | |
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| 1.0476 | 2.63 | 3000 | 1.1292 | |
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| 1.0688 | 2.72 | 3100 | 1.1291 | |
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| 1.0583 | 2.8 | 3200 | 1.1289 | |
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| 1.0545 | 2.89 | 3300 | 1.1289 | |
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| 1.0744 | 2.98 | 3400 | 1.1289 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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