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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_sft_router05_lr1e-4_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_sft_router05_lr1e-4_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: 2.1035 |
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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: 0.0001 |
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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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| 2.2617 | 0.09 | 100 | 2.2410 | |
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| 2.2246 | 0.18 | 200 | 2.2165 | |
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| 2.1994 | 0.26 | 300 | 2.1994 | |
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| 2.1767 | 0.35 | 400 | 2.1869 | |
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| 2.1532 | 0.44 | 500 | 2.1792 | |
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| 2.171 | 0.53 | 600 | 2.1717 | |
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| 2.1588 | 0.61 | 700 | 2.1645 | |
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| 2.145 | 0.7 | 800 | 2.1567 | |
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| 2.1366 | 0.79 | 900 | 2.1507 | |
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| 2.1219 | 0.88 | 1000 | 2.1450 | |
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| 2.1415 | 0.96 | 1100 | 2.1387 | |
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| 1.9765 | 1.05 | 1200 | 2.1446 | |
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| 1.9837 | 1.14 | 1300 | 2.1430 | |
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| 1.9952 | 1.23 | 1400 | 2.1388 | |
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| 1.9868 | 1.31 | 1500 | 2.1351 | |
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| 1.9864 | 1.4 | 1600 | 2.1316 | |
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| 1.987 | 1.49 | 1700 | 2.1263 | |
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| 1.9678 | 1.58 | 1800 | 2.1230 | |
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| 1.9827 | 1.66 | 1900 | 2.1164 | |
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| 1.9846 | 1.75 | 2000 | 2.1134 | |
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| 1.9694 | 1.84 | 2100 | 2.1068 | |
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| 1.9429 | 1.93 | 2200 | 2.1035 | |
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| 1.8079 | 2.01 | 2300 | 2.1369 | |
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| 1.8132 | 2.1 | 2400 | 2.1375 | |
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| 1.8043 | 2.19 | 2500 | 2.1360 | |
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| 1.7927 | 2.28 | 2600 | 2.1334 | |
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| 1.7935 | 2.37 | 2700 | 2.1335 | |
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| 1.7982 | 2.45 | 2800 | 2.1321 | |
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| 1.8029 | 2.54 | 2900 | 2.1311 | |
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| 1.7919 | 2.63 | 3000 | 2.1298 | |
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| 1.7953 | 2.72 | 3100 | 2.1287 | |
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| 1.798 | 2.8 | 3200 | 2.1280 | |
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| 1.7947 | 2.89 | 3300 | 2.1282 | |
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| 1.8015 | 2.98 | 3400 | 2.1283 | |
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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 2.16.1 |
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- Tokenizers 0.15.0 |
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